{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.306404193Z",
     "start_time": "2023-06-19T09:43:25.915646076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<contextlib.ExitStack at 0x7f2dd45057c0>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import torch.backends.cudnn as cudnn\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "from PIL import Image\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "cudnn.benchmark = True\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "# the same as regular one but with shift to grayscale\n",
    "data_transforms_gray = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.RandomResizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.Grayscale(3),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.Grayscale(3),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}\n",
    "\n",
    "\n",
    "data_dir = 'dummy_data/hymenoptera_data'\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "image_datasets_gray = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms_gray[x])\n",
    "                  for x in ['train', 'val']}\n",
    "# here changing back and forth between grayscale and regular image_datasets\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets_gray[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=4)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.346774609Z",
     "start_time": "2023-06-19T09:43:27.313651594Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Display image for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.654140795Z",
     "start_time": "2023-06-19T09:43:27.336060560Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    # Create a temporary directory to save training checkpoints\n",
    "    with TemporaryDirectory() as tempdir:\n",
    "        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')\n",
    "\n",
    "        torch.save(model.state_dict(), best_model_params_path)\n",
    "        best_acc = 0.0\n",
    "\n",
    "        for epoch in range(num_epochs):\n",
    "            print(f'Epoch {epoch+1}/{num_epochs}')\n",
    "            print('-' * 10)\n",
    "\n",
    "            # Each epoch has a training and validation phase\n",
    "            for phase in ['train', 'val']:\n",
    "                if phase == 'train':\n",
    "                    model.train()  # Set model to training mode\n",
    "                else:\n",
    "                    model.eval()   # Set model to evaluate mode\n",
    "\n",
    "                running_loss = 0.0\n",
    "                running_corrects = 0\n",
    "\n",
    "                # Iterate over data.\n",
    "                for inputs, labels in dataloaders[phase]:\n",
    "                    inputs = inputs.to(device)\n",
    "                    labels = labels.to(device)\n",
    "\n",
    "                    # zero the parameter gradients\n",
    "                    optimizer.zero_grad()\n",
    "\n",
    "                    # forward\n",
    "                    # track history if only in train\n",
    "                    with torch.set_grad_enabled(phase == 'train'):\n",
    "                        outputs = model(inputs)\n",
    "                        _, preds = torch.max(outputs, 1)\n",
    "                        loss = criterion(outputs, labels)\n",
    "\n",
    "                        # backward + optimize only if in training phase\n",
    "                        if phase == 'train':\n",
    "                            loss.backward()\n",
    "                            optimizer.step()\n",
    "\n",
    "                    # statistics\n",
    "                    running_loss += loss.item() * inputs.size(0)\n",
    "                    running_corrects += torch.sum(preds == labels.data)\n",
    "                if phase == 'train':\n",
    "                    scheduler.step()\n",
    "\n",
    "                epoch_loss = running_loss / dataset_sizes[phase]\n",
    "                epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
    "\n",
    "                # deep copy the model\n",
    "                if phase == 'val' and epoch_acc > best_acc:\n",
    "                    best_acc = epoch_acc\n",
    "                    torch.save(model.state_dict(), best_model_params_path)\n",
    "\n",
    "            print()\n",
    "\n",
    "        time_elapsed = time.time() - since\n",
    "        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')\n",
    "        print(f'Best val Acc: {best_acc:4f}')\n",
    "\n",
    "        # load best model weights\n",
    "        model.load_state_dict(torch.load(best_model_params_path))\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.662404794Z",
     "start_time": "2023-06-19T09:43:27.660888763Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=6):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title(f'predicted: {class_names[preds[j]]}, actual: {class_names[labels[j]]}')\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.705618865Z",
     "start_time": "2023-06-19T09:43:27.666650252Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "512"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft = models.resnet18(weights='IMAGENET1K_V1')\n",
    "num_ftrs = model_ft.fc.in_features\n",
    "num_ftrs"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:27.851463249Z",
     "start_time": "2023-06-19T09:43:27.675822199Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "model_ft.fc = nn.Linear(num_ftrs,2)\n",
    "model_ft = model_ft.to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# observe that all parameters are being optimized (what does it mean?)\n",
    "optimizer_ft = optim.SGD(model_ft.parameters(),lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:43:28.915428174Z",
     "start_time": "2023-06-19T09:43:27.891534798Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "----------\n",
      "train Loss: 0.7830 Acc: 0.6434\n",
      "val Loss: 0.6232 Acc: 0.8105\n",
      "\n",
      "Epoch 2/25\n",
      "----------\n",
      "train Loss: 0.6306 Acc: 0.7500\n",
      "val Loss: 0.4044 Acc: 0.8301\n",
      "\n",
      "Epoch 3/25\n",
      "----------\n",
      "train Loss: 0.6931 Acc: 0.7418\n",
      "val Loss: 0.5786 Acc: 0.8235\n",
      "\n",
      "Epoch 4/25\n",
      "----------\n",
      "train Loss: 0.5918 Acc: 0.7541\n",
      "val Loss: 0.5053 Acc: 0.8039\n",
      "\n",
      "Epoch 5/25\n",
      "----------\n",
      "train Loss: 0.6514 Acc: 0.7500\n",
      "val Loss: 0.4682 Acc: 0.8431\n",
      "\n",
      "Epoch 6/25\n",
      "----------\n",
      "train Loss: 0.5198 Acc: 0.7992\n",
      "val Loss: 0.9576 Acc: 0.6797\n",
      "\n",
      "Epoch 7/25\n",
      "----------\n",
      "train Loss: 0.6470 Acc: 0.7623\n",
      "val Loss: 0.3813 Acc: 0.8627\n",
      "\n",
      "Epoch 8/25\n",
      "----------\n",
      "train Loss: 0.4312 Acc: 0.8361\n",
      "val Loss: 0.3076 Acc: 0.8824\n",
      "\n",
      "Epoch 9/25\n",
      "----------\n",
      "train Loss: 0.2866 Acc: 0.8934\n",
      "val Loss: 0.3468 Acc: 0.8824\n",
      "\n",
      "Epoch 10/25\n",
      "----------\n",
      "train Loss: 0.2292 Acc: 0.8934\n",
      "val Loss: 0.3697 Acc: 0.8889\n",
      "\n",
      "Epoch 11/25\n",
      "----------\n",
      "train Loss: 0.4070 Acc: 0.8320\n",
      "val Loss: 0.3022 Acc: 0.8954\n",
      "\n",
      "Epoch 12/25\n",
      "----------\n",
      "train Loss: 0.3117 Acc: 0.8689\n",
      "val Loss: 0.3416 Acc: 0.8889\n",
      "\n",
      "Epoch 13/25\n",
      "----------\n",
      "train Loss: 0.3877 Acc: 0.8320\n",
      "val Loss: 0.3617 Acc: 0.8889\n",
      "\n",
      "Epoch 14/25\n",
      "----------\n",
      "train Loss: 0.3613 Acc: 0.8156\n",
      "val Loss: 0.2953 Acc: 0.8758\n",
      "\n",
      "Epoch 15/25\n",
      "----------\n",
      "train Loss: 0.4041 Acc: 0.8074\n",
      "val Loss: 0.2763 Acc: 0.8758\n",
      "\n",
      "Epoch 16/25\n",
      "----------\n",
      "train Loss: 0.2401 Acc: 0.8934\n",
      "val Loss: 0.2840 Acc: 0.8693\n",
      "\n",
      "Epoch 17/25\n",
      "----------\n",
      "train Loss: 0.3015 Acc: 0.8525\n",
      "val Loss: 0.3002 Acc: 0.8758\n",
      "\n",
      "Epoch 18/25\n",
      "----------\n",
      "train Loss: 0.3187 Acc: 0.8770\n",
      "val Loss: 0.3053 Acc: 0.8758\n",
      "\n",
      "Epoch 19/25\n",
      "----------\n",
      "train Loss: 0.3076 Acc: 0.8689\n",
      "val Loss: 0.3693 Acc: 0.8889\n",
      "\n",
      "Epoch 20/25\n",
      "----------\n",
      "train Loss: 0.3035 Acc: 0.8566\n",
      "val Loss: 0.3131 Acc: 0.8758\n",
      "\n",
      "Epoch 21/25\n",
      "----------\n",
      "train Loss: 0.2401 Acc: 0.9016\n",
      "val Loss: 0.3190 Acc: 0.8889\n",
      "\n",
      "Epoch 22/25\n",
      "----------\n",
      "train Loss: 0.3108 Acc: 0.8689\n",
      "val Loss: 0.2964 Acc: 0.8824\n",
      "\n",
      "Epoch 23/25\n",
      "----------\n",
      "train Loss: 0.3007 Acc: 0.8730\n",
      "val Loss: 0.3284 Acc: 0.8889\n",
      "\n",
      "Epoch 24/25\n",
      "----------\n",
      "train Loss: 0.2642 Acc: 0.8730\n",
      "val Loss: 0.2881 Acc: 0.8758\n",
      "\n",
      "Epoch 25/25\n",
      "----------\n",
      "train Loss: 0.2490 Acc: 0.8811\n",
      "val Loss: 0.3073 Acc: 0.8693\n",
      "\n",
      "Training complete in 0m 43s\n",
      "Best val Acc: 0.895425\n"
     ]
    }
   ],
   "source": [
    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
    "                       num_epochs=25)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:11.645802921Z",
     "start_time": "2023-06-19T09:43:28.917029414Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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wwQdxySWXYO/evWo2N/j9w+9FbYXBbxePPvoo3vnOdxrF8HuO32pthcHvB774xS/+3x6CwW8By3IrDAwM/nBg3AoDAwNHGOVgYGDgCKMcDAwMHGGUg4GBgSNMtsIFXn75ZTCOa1mWajwit1mWBa/Xi0gkAq/Xi3a7jVarBcuy0G630Wg00Gg00Gw20Wg0UCwW0W63Ua/X0Ww20Wq10G631TV5TkI2qOGP1+uFz+eDz+ez1bV4vV41Psuy0Gw2USgUUKlU0G63VWViMBhEKBRCLBZDJBJRNONarYapqSmUSiU1pna7rcbebrdRq9VQr9dx+PBhHDp0CIFAAB6PR503GAwiEAjA7/ercYZCIVUZyfG2Wi0EAgE0Gg11T9yff8vGPJVKBblcDl7v/DxXLpcRCoXQ39+vzmtZFt7//vf/n3odfm9hlIMLtFot9TLyZSUowHp3KV3YJSgw/JxC7JRI4naPxwOfz6e2UyF4PB60223btXkMhXhubg6FQgE+nw9+v1+dj4ohFAohFArB7/fbxs1rtttt228qwlarhUqlYhNmYL5bFsfo9XoRDAbVZ61WCx6PB36/39YdyrIslEolNY52u41AIKCuxeceDoeRSCSUsmk2mwgEAqrql9cxWD6McnCBubk5RCIRBINBmwACcLQgCKfPKJhUDnyZpUUilQbPQ1AQ9QpYHg8sKKZqtYpCoaCsFADK0ohGo4jH44hGowgGg47WBxUCFQrH0263YVmWOj/3p2BTgbRaLWU9SQFvNpsoFouqqtLj8SCZTKJWq6FarSqrg0rZ7/crher1epFOp9V2+Yw5LqMg3MEoBxc4fPgwMpkMstksQqGQmsHli0lQ4HXBly+sZVk2K0C6Abo1QUXC61FApHXAc1A4ms0mKpWKahoi3Rufz4d4PI5EIoFoNIpIJKKEmfcSDoeVC9RsNpXCkia+3+9XhUu0InhfgUBAWQ20XoD5KkuPx4NyuYxisaga5LTbbUQiEViWhenpaXWdSqWCYrGIcDiMaDSKcDiszg3MK0FpsfC5GLiDUQ4uUCqVUK/XMTMzg+7ubqTTaTUDS8sAgDLxdSEn5OwsZzrOlPwB4GhVyGvJvymcjUZDzcDNZtOmQPx+v3IlEomEmukp3LQSgPnqwVqtZlNa8l78fj+6u7vh8/mQy+XU9Wq1Gnw+ny3mQCuiVqvBsiwVZ6HbEg6HEQwG0Ww2VTwEmI8vMLZRLBYRDAaRSCQQDodV7ILPj0pCKl2D5cEoBxegcJTLZYyMjKBcLisrQs7m7XZbCSKFFYD6nD49BbFWqylh5IsulYpUDnIcwLySkRZBq9VCtVpVgT2OhTO+FCAqCbpJPCeFkjNyLBaDz+dDuVy2XZf3Gg6H0d3djUQigWKxqIKetVptkcDyOs1mE+FwGNVqFblcDu12G6FQCOl0GpZlIRqNqmvS4qByoUXUbDZRLpcRDAYRjUbV90AYt8IdjHJwAQqZz+dDs9lELpdDsVhEJpNBPB5HKBSytcGjeSvNbj2QCcz3W+zr60MsFrO5ENJtWCozQkGr1WqYnZ1FsVhU2QC2GJOBSt03l+4KYA9wUjHF43HlPjBLwX1kPCIWi8Hv9yMajdqEl1aEDKhGIhHllgDz1kE4HFZKLBaLKUUsLSWpbBi8nJubQyAQQDKZRDKZRDAYVJaQwfJhlIMLSJOaLztdjVKphI6ODqUgKNzAgvVAYZCzbzAYxPDwMJ577jls3boVa9asUQKjuw16MNPj8WB2dhaHDh3CzMwMKpUKotEo0uk0UqmUivLzWvxN4WKWQY5XFyiZneBML7MQ0rLh/UgXJRaLodlsol6vq/iF1+tFKpVSAdHOzk40m03E43GlIJjN6OjoQDAYxMTEBOr1urJ4qKBpfTWbTczMzKBcLiOdTiORSLhaKtHAKAdXoHKQaUPJH2g2mwiFQkgmk4jFYmi1WqjX6+oFJp9Bcgx8Ph86Ozvx9NNPY2xsDG9961uxYcMGmxLhtQlaDPV6Hb/+9a8xOTmpuAm8HgVP+u5SQdBEr9frqNfrSqD1+yOYimSQUiop/dxer9cWzGRsgXGGRqOhZnqfz6fWcUgmkyrQyxiDZVlIp9MIhUKYnJxEvV5XXAoqB/l8arUacrkcyuXyb6TZ7x8ijHJwCSkQwMLs2W63UalUUKlUUCgUEIlEEI/H0Wg01CwqA3BMIfr9fvT09Kh9nn32WYTDYfT09CziUVA58VxDQ0MYHR1VQTyOhaY8Z3HO7rQadB4ETfdYLIZwOKz2l3EPEpui0agSWl6TAgosZEskh6NarWJqakrFNqgwSqWSjSxWLpcxPT2tGvnKTE8kEkF/fz/q9TpqtRrK5fIiS0daVsViUVk4BsuDUQ4u0Wq1bCw+PQtBk5j+Oc1y6UoAUDMpMD9jMvoeCoUwOjoKj8ejSD6SyFSv11Eul3H06FGMjo6q68vAJ90SBj1lylUqHLI1gXmhpgUhFYhkYVqWhVAopNZPkEqBkIFKYN6SISYmJtBqtdDR0YFMJqNiNul0GslkUhGeyuWyen7MSDBOsWLFCpWJ4f3l83n1zKQSldc2OHYY5eACNLVlipFCI10O7kvB4+cy6yAzDj6fD9lsVrkZc3NzeO6557Bu3TqEw2HkcjmMjo5idnZWBfyogCQxiWQhSQyStGdgQdiZIWk0Gip7Uq/XEQwGlbWjH8dUazgcRrlcVhaEvH9CkpxarRbC4TA6OzsxNzeHzs5Opeg8Hg+q1SoSiQQ6OjpQLBZRr9cRCoWQy+UwPT2NaDSKbDYLn8+HSqWi4h/McITDYUxNTSlrgtc3XAd3MMrBBaTZKok2sn5BzvSSESgVBIVWzryMzvOc09PTePLJJ+Hz+VAoFFCr1RRJiDUI8tqEzADoFgCwQAGnMqhWqyouQX6EzETINCwzFlQQjJ9QKersyUajoWZ31lpEIhF1LBVbo9FAoVBQQcpsNot8Pq9iNUyllkolNa5QKKTuZWZmxqaI9Xs2WB6McnAB+bLpQikZkTIVKAOXwMLLS7OcbgpncVoEdB94TcmhyOfztjoCnpcCHI/HbSlJHZITQWIRU4vMCHAGpltEBcdZm4qgUCjYLBdJ2abyoHKg20QLJBgMolKpKOvh4MGDCIfDyGazKpgpCVITExM4cuQIMpmMonuPjo5icnJSKVr5vJZaXc3g9WGUgwvoDEVuk+k8AIuYkZIfIS0J6fMDC24LmY2SFiyF1ev12uIePLff70dXVxei0agtViAzD5I0JYlSkl8hOQnymhyPx+OxBS6LxaJiRlLBVKtVAEAqlVL0bT2tyoBpo9FQnzPgSEuJFkM4HEY6nUahUEAul1PnYOZEQj5jg+XDKAcX0C0AYKG4Sd8uBVL3gyl4ZDNyFSNmACQTkaYzBZameaVSUbNlIBBAJBJBZ2cnUqnUIuFwIjtROfCaFMRgMIh2u61oz7qCkUqKNGa/349isagqMyWj0efzIZlMIhwOq1WomKHg2JnZYGEYyVz1el0xJZvNJiKRCCKRiFKoHP9SFp1xK9zBKAcXkOlAWgO6sMviI5razBY0m02lCGTWQtZncJaVPj9nbo/Ho3z2SCSiZuu+vj5VI+GUqpRZFRkfoNUgrQ+a67w/yZQE7HEMyb0gFbtUKqn7ZjDS7/erwi66IbFYzDYOUp+LxSLGx8eVovB4PCgUCsrKkpkfPkcZ7CX0/QyOHUY5uIReGSlTkk5mLGMIeqZCxiNYwWhZFmq1mi29KDkGkhYNQM3Gk5OTiMVii1wbfVYl61DuIz/z+XwoFovIZrNqfFQG0nrh3wx8MgjKpjFSic7MzKjnwiKp2dlZlMtlFefgD++tXC4rq0rS1YPBoC1DQujVpHzORjm4gyGdu4Cew5cvJN0LGaHnzCmPlTO3nJ0TiYStElO6KmQcBoNBm3IB5gWuUCjg0KFDqtpRT6nq98CxS+uHY6fPL8ct99VnZCoKmY1IpVKIRqOIxWJqzUpZ7ZlOp9Fut1EsFpVLw2uxiCsWi6FWq6FSqSjK9uTkJAqFwqIYiVPg1SgH9zCWgwtIoZQvo+QS8MVluk1aCVKp6Of1eDzKbNYDkDT15bGSIxEIBDA1NQWfz4d169apsm9mReTsvFSQjp+xiIvuEc19xiHkOHRFxGwJiUvValVZNLI/QzAYRDqdxuTkJKanpxVtmsIei8XUwrVTU1OL4jpyzPyRLtBSWRqDY4NRDi4gsxAytsDPdCFn4xKaxnyB5W+v14t8Po/Dhw8ry0EGANnYRL788pqSqjw5OQmv16uKtySPQnZmWmqWZSk0swZMITIwWavVVABR7xiln1e6H9FoFABUQJIKLZ1OY3x8HLOzs0ilUjYF4ff7MTg4iMnJSRSLRVuA1elaUiHIdLLB8mGUgwtIxUBhArBo5iJZiEqBjEPGFCzLUjGGZrOJAwcOoFQqqZw/A5TMCOgztBwPBTCTyaBarWJubg65XA49PT2LxsyZ28l6kIVhABT7stFoqJQjA6N0IaRLocdiOH4+l2g0akvByjEXi0UUi0VYlqXiEjy+r68PxWIRMzMzi8rP5X1Id0n+b7B8GOXgEk5ugnxRJTeByoFKQO7LY8fGxjA8PKzIRzTbpSBLIXCiYAcCAcTjcWSzWRXIKxQKqoCJQkWlxOMl94HXkpWkshyaszmtGAopA5CSdCUJSFSKVE4sPmNwlQqOKdxms4lYLAZgwdpJpVLw+/2YnJy0xRtkJkXGZ6RCNFg+jHJwgXK5rMxqvWYBWBAyPQZBF0QGNFutFkZHR7F3716Uy2UVp2DaUyoQJ6tBms3MHMgOTwwskqxEoQcWCrNkcJTCJdvFS3dD+vfSQiK7k+Pm9VmVyswDazb4PILBoGqQQ6X28ssvK/IXy7l5PSq6XC5nK6iSAUndxTMBSXcwysEFaPq2Wi0cPnwY1WoVoVAIvb29SKfTKhUneQEEX+BqtYparYajR4/ixRdftBVxSZ9ZT8s5zZB6xJ7XsSxLNWXt6OhQKUNaENJNkNfmuXT/Xldw3E7LgnEWWhHAPPsxmUzCsuZLwhkXaTabKJVKyq1inQjjGpZlqWeUzWZVXQX7Pvj9foyPj9tSyPL5ymdj4A5GObiAnGGz2SyefvppjIyMqErJjo4OdHd3K3+fzVFY7VipVDA8PKz6K0ryD7D4pZZKRlohwIIwsF27nD2r1SrGx8dRq9UwOTmJ9evXI5FIqPFz5uXMKtu989yS4OWUHqVSkO4H+RhSQSQSCZUmbTabmJubQ6lUUsqEiqFSqSjXitbK1NQU2u02EomE2s4OT+Pj4zYLQs9ccJvB8mGUg0vwBQyFQjj55JPh9/tx8OBBzMzMYGZmBkNDQ6r0mn41zX6dvswZUZZX6/wDeU3O1lKJSJeCAlypVDA7O4vZ2Vk0m03k83kcf/zxqjMSA4+yQ7Osn6Ci4I9e3UlLQ1ocujtCYaayYm/LYrGoFATHywCu7gqQSu3xeFTcAZjvf+HxePDaa68pl4djc/ptsDwY5eACkgrNNN9JJ50EABgaGlIvqqRR08+mMgAWLzyjp+HeKKevz456kJKZDzZMmZycxFNPPYU/+qM/Qk9Pj43CLRUCYVmWbXUqZhekBSEDoqRIc2ytVgvlchn5fB75fN7Wir5er6NSqSCZTOK1115T8YVCoWBzsfgMms0mZmdnYVmW6ung8cwvflOpVDAzM2Mbt1QMJubgDkY5uICeOaAZfeKJJ6LdbuPVV1+1cQsoPBQYzqh6dkPfJq8jKyGdziu3AVCmO90WClqxWMRTTz2Fvr4+rF+/Xq01QcGX1gB/mEZdijMgqc2srGw0GiiXyyiVSotYlrJSs1arIRQKqSpLwJ6hkVyPdruNQqEAj2e+4Syfe19fn2Ki8jr68zBYPoxycAG+uHpOPRQKYceOHQgGg3j55Zdtx1DQZTGW5DHI8/Aar0f4kfs7Ce3s7KwqbtLPxUDq5OQkuru70dfXh76+PmSzWeU+eL0Ly9kxK8PrkF4t+1Dwb8nrkBaHvAe5OlYikUAwGEStVlPrd8rnpQu3Zc33jvB6vchms6qOo6+vDyMjI4tawsmUpsHyYJSDS0ihk+a03+/HmWeeiXq9jpGREQAL/SQBexMSpvukz667HDqZRwqMVBiyazRdiEAgoISY45TjqNfrOHLkCCYnJ/Hyyy+rNmzZbBYDAwPo7+9Huz3fsIXWgHSRCOnmyOcjf+u1Dww+UoGxVdxSlpN0d2hBkBwWCASQSCTQ19eH0dFRNSYnt8zg2GGUw5uANOt1RXH66aer5eGdUm16cFEGBeV+MjOh12XwvIzqy+uwUS0Llui3W9Y8+3DLli3Ytm2bSquOjY3hueeeQy6XQ1dXFwYHBxEKhdQiOVwgl2MDsGjMEnwu0kWQcQB2sGbGRrpa8joy7iDP1Wq1MDMzowrRfD4fOjo6kM/nlXshvyeD5cMoBxeQfRykb84Xm+y+HTt2YPfu3bbl4KTA6Aw+ySWQAiV5BXoQkPtI5iXHNzc3p9q0cb+BgQGccMIJGBwcVIVc4XAY/f392LdvHxKJBM477zysW7dOuRAUXt090K0bQo5Rd2l4vyRExeNx5Y7ox8rzOTFF2+02crmc6g7l9/uxcuVKjI+Po1Kp2MhWBsuHUQ4uoM9wFFRZR1Cv19HT04Pt27fjmWeesQmWZE7K3oxUGHqjFkIXSKkcpAnNbs+M/NMyCQaDWL16Nfr6+hT5iMHB2dlZbN68GWeccQZWrlwJy5pf8+GFF16wLe+nKwM9FiLjKboVoLseXq9XcRfm5uYW9ZlwijfIv8n2HB8ft6WBV61ahWaziaGhIbNmxZuAUQ4uwfiCDPKxMpK05XK5jI0bN2J0dFS5F7og61wAvT6BkLEH6WYwniC7I83OziqCEXsyUHjq9boK6LH1GlmHV1xxhWqk8tprr+EXv/gFDhw4gJNOOkkRumTWwklx6c/IyY2S1oTH41HdoNj3wslykNfQXbl4PK7+ZxpZ54cYLB9GObwJ6KlHWdgkTf/BwUHVVl53EXRwRpRKQp5PCpm8BmnSwWAQ09PTNj4BYK/9mJycVLUQ8Xgc6XQa0WgUpVIJw8PDOHDgAPbv368Wvp2YmEBHR4ejNSAtB2kB6c+J+8t7l8+Oi++yB6UUcD2b40Qf58JBhUJBxVjkuhsGy4dRDi6g+8CyjoKmLoWgVqspOvXo6KitpJsvvXQPCJ0hKLMcVCASlmVhbm4OHo9HBSHZ/4CNXLl8XDgcxtGjRzE1NYV8Pq+arJBoJFfpAuZdjunpacWs1GMP+t/SUnAKMsr74DaSycj0ZO9IfT9ei/D55pfhq1QqmJychGVZb8jLMDg2GOXgAnyZ9X4N/Ez2G+C2DRs2IJ/PY25uzqYgZF9J/Rp6ynIpS4OfsW6BhCDLstDZ2YlQKKSuOz09rWIS09PTqNfrmJmZWVSEJZehq1QqOHz4sOIkvJ7FoHMbpDJYClQAvD77RMzNzdmCqdJ6IhhQZXu9SqWCdrutKlyd3B2DY4OxuVxACgIpyDLa7uSTJxIJDA4OqroAxgskeUi2QWN6UhKMqEzkPrpvzlhHrVZTq3zTImg0GpiensbIyIgqWKJQcnGcdDqNSCSiukgDUIvdjo+PL8qWyHvmdlmLQSUqFY+0KHTLwuv1qpW3e3p61PKAPE7GYxhQpYKr1+uIxWKIx+Nqf6d1PA2ODcZycAFpOQCwtXDXA4zMFFiWhf7+fuRyOQwPDytlIveR3AAZvAQWOisD9myJzHBIRUOCValUUutZBgIBtc4mA5Q+n08tref3+9Hf34/Vq1fjiSeeUKZ9NBqF3+9HPp9HJpNRi91KarN8NrwHKhwyLUnllkrFySri/YdCIcVhYDaDz0yWiDO2kMlkEA6H0Wq11DJ6enDT4NhhlIMLOPEb2JCV4Asvg2mBQABbt27F+Pi46p7Mz2QKUOc/yMCcbpHIyD0FidYArY9yuazGQUFitkW2ofP7/WpZOVZPAvPBQjarHR4exqpVq9S9rlixwqbM9IYzwWAQqVQKgUAApVIJxWJRpRd5r3pMQc9sJJNJBAIBFAoFZe3wuTWbTbWeBeM7ek9PE3dwB6McXEIScSQ3QTfzdRO6o6MDvb29trz+Utx/KThSEUmTXgZEm80m4vG4bRUsZj7YTKXdbqveEew/wR4U5AzMzc2pNnB+vx+JRELFAl577TWMjY2p1GxXV5cKZlI58HqhUAiRSERZHoxXsBW9TD9KIph8djwnA5aVSgXlclmRnACoWAu7YnOhXZOpeHMwysEFZDcjKgdJaJICLV0NdnVeuXIlRkZGUCqVFjVLdaqylNkQPUhJcCaPRqPo7OyE1+tFpVIBAMVdaDabKrYhax08Ho9al7K3txe5XE4FWxmvSKfTqpXb3NwcxsfHba3vZcEWFQmX56MlwbUqAChOA5UeYK+HkM+Dz5LWTSwWU70q2E4OmFeEU1NTNmvBuBTuYZSDC9AFYBGSnO04Q+vxAb7wzWZTtZOTi8YAsFkS8uWm0NFdcCL38NzNZlP1WaxWqyiVSqqhSjgcVvt6vV612nUgEECz2UQ0GlXCyKX4IpEIjh49irm5ObXITDKZxMDAAHp6etDZ2amW8pNuCvtH8vy0rOSKXrw/ukBSKTilPKX1FAqFEIvFUCqVFIdExhecgsIGy4NRDi7AtupORBvOmnzRZQaCxwSDQfT29iqyEmdz7u+UspPkKqls9OYshUIBgUBAdXA+evQoLMtCKpVSvnulUlFxBnIL2BRmZmbGxo/gvuVyGSMjI+ju7kZvb69aNXtkZAS9vb2qKQwDoVQ8jIPwx7IsZU3wnqlo9W5OFGzZ3UoqTboRiUQCpVIJs7OzNtKXnnI1WB6McnCBdrut2IM6i4/BSs6UcuFXmtEUOK6ExeP01JvuSsgejXr8gUqCsQI2kw2Hw5iYmECr1cLAwAA6Ojrg9XoxMzOjYgO0MEik8vl8ilpNS4UZmWq1isOHDyMWiyEcDuPZZ5/FzMwMtmzZorIgrLgk+UoXUMY7aHHIuIOentWVglMcgQo5HA5jbm5OxTT4mbEe3MEoB5egkDKFKBUE/6eg8OWXMz97IZLNJ5WHZAZK4ZJms26C8zO59F53d7dyc9jUtlarYdWqVSpwSQXGtGcoFEIymUS5XLaNiahWq6rNfDqdxooVK1Cr1XDw4EGsWrVKuRJc6VuOUzZeoYURjUYRCoUUgYnXlSlhGZOhkpDnkVkSloHn83kVjzCBSXcwysEl5EwmrQcWXdH10PfnTNzT06Oi/NIPl8E5KVj633orexZXNRoNxONxRVwKh8PYsGEDpqen1WrVL730kqqVYJyg2Wwq2nc+n1ckKC6KS+uHAdSpqSlUKhVkMhnE43HMzMyg0WggkUioczPbIWMF8ofX4HKBVCpsUEOlSSKYVA56dyoqUbIro9GoWkGLgVmD5cEoBxdot9vKl5bpOADKl6c7IWdPYEHo5WwmmZYyTuGkgKTZLZu/8P9SqYTe3l4AC+Sszs5OBINBjI2NoVAoIJ/Po91uY/Xq1Uqw8vm8bV0Juj3MSFDoeF/sHD07O6va0FORZLNZJBIJFYjU05TM7NB64H2zcUutVrN1oWaWR5Zf6ylifRszJd3d3Tb+icGxwygHF6DAS5YkuQWcLYPBoOqVoNOKvV4vZmdnFwUfeS7m7XXfW9KWda4DFQnrEbgmJV0DVm1yTBzryy+/DK/Xq1KLMrPA5q+MY3CBmlwupxQCsx7RaBQzMzMYHh7GunXrlILS2Zzcxt/6gjrkM8g0MQvaJDNSBibl+eT1GANh+tRgeTDKwQVk3QCj9B6PR/niMqUpc/DSNZibm7MpAL7kJAoBC/UXUglIXoWMQZBHUS6X8eqrr6oWcEyNMt1HIWc9wtzcHHp6etDd3Y3JyUn4fPML23I8sVgMAwMDmJ2dhdfrRXd3tyrkKpVKapYfHBxEo9FAsVi08R+WaiXHWIwTeYzU7larpc6vE82kgpCuhfx+JIXbYPkwysEFmGeXZjP7LUqmYCgUUlWCMjbAz/XKTemX60pBQlcM+oyaSCTUeg5cUo7KKRKJKKVUKpUQjUZVLIJCWavVlPWTTCaxZcsWTE1N4fDhw1ixYgVWrFiBZ555RvEq0uk0Ojs70dHRgfHxcTz33HMIh8PYvHmzui8ZW5EZHVkFyucALCg7vYWcpJfrWQzZYp/ukVxkx2B5MGFcF2DgTsYcWEkoTXm+pHpcQAqynPkB2BSLrMvgcdIVkS4G3Zw1a9agu7sbR44cUUJeLBZRLpeRzWYRjUYRCATQ0dGBdrutFrFlloBrWFKh1Ot1jI2NIZvNore3V2VD5PobABCJRJBIJLBhwwYAwL59+1AqlQBAnadcLitrSvZv4P1LbgiP4f6S3KSzMWVVKVOoZFPKDInB8mAsBxdghyWZNmS2IBaLKbaeVBBSqKVrIGc+PRovl7mXSkAqBR7Hmfq4447D7OysYkMyFhIKhbBp0ybkcjkUCgXEYjEVVOW1KpWK8u9ZyMSgKhVToVDASy+9pEqko9EoPB6PIh+lUils2bIFzz33HA4dOoRUKgWPx4NSqYR2u41wOKxcBLlSuR5DkSuEyYCsHp+RjExpiQCLFyI2WB6McnAB1hswuk5LotlsqpedboFec6FzFiSTj+A2rlbt8XhUzt4pHZhOp7Ft2zasXbtWuQPAQjl4OBxGJpNBOp1GT08PyuWyKt2WVoNOGGKvBDInPZ75fo+HDh1CvV5HKpVSRVsy2Lp+/Xrk83mMjY1h48aNaLVaKhAKQF1XZkb4vMi5oPDzeUkFIvkjcsFePfBpCFBvDkY5uAAVwOzsLLq6umzCXqvVbEFKpt9oNtPfpv8t03uAPfYgG6WEw2HVgr1Wq2F4eBh+vx+Dg4MYHBxUq1hLU5/MxmQyie7ubqU0UqmUSnMmk0lMTEwoCjLNdboBnMXn5uaUFREIBJDJZOD3+9HV1aXIT3wugUAAa9euxdjYGF566SW1XB35FKQ96wFE+QxkwFe6FnSfpMulk8Xkdp7PYPkwysElSCVmoE2a/s1m01azIAlRFH4Sc6hYeKzTClibNm3C1q1bkclkEIvF4PV68cwzz6BYLCKTySizW58xW60WSqUSJiYmsHbtWsRiMXWdWCymshIAsHLlSlQqFYTDYXi9XgwNDam/aaGEw2FYloV4PK46TM3MzGBgYADJZFK1pwsEAooB+utf/xo+33x3a46TCkaOU6eDAwtxmHA4rKwHuQCQrGiVCkKeQ7pqBsuDUQ5vAlystru7W728MqAWCAQQj8dRKpVsZjUA5YPLGY5xAb/fj7m5OcRiMbXYLWdaWgPnnnsucrkcxsbGVFxBLnUnXRbJJ5AR/VgshkKhgEgkglwuh3K5jFAopCodKWC0jiqViirWYswlFoshlUrB6/WiWq3aXIJwOIyNGzfi8OHDqFar6O3tVdkDJ8akHluQ3BDAzpl4vSCjtMZkitNgeTDKwQX4EjOyX61WVXCSyqFcLqs6Awq1XA+SpCNgoY9BNBrF9u3b1UIvNJ+lwHD2DAaDWLVqFWKxGIaGhtRyddK1oJBQWDnrypQnA4mlUgn5fB7RaBSNRkMFK1lr0Ww2Ua1WMTAwgGq1qgTu1VdfRTwex6ZNm5TrI6su6UKNjY1hYmICmUwGmzdvRjabtT1PvbeD7i7ItK2s5pQ/so5CUqoN3MEoBxeggNbrdcV27OzstPEYWB/AaD7dAdZP0P2Qs+Hc3Bx+9KMfoaurC93d3ejs7ERPT4+qPZA+NyP9pEYfPXoUk5OTyoqgoorH4yqe0Gw20dHRoc4TDAbR1dWFmZkZVWXKXhRMC/b29tqo4QxwhkIhRKNRTE1N4YknnkC5XMaGDRvg8/mQTCaVS8KGryzxnpqawsjICNasWaOepeRC6DEDuYaHTKFKRSnvV29ko2cwDI4dRjm4gJ5hYNpN9ihgMLJer9tW4GZAkqlGli1z9szlcsjn8zh48CB8Ph86OzuxadMmrFmzRikaneRDYcxkMjhy5IhKM9I66OnpUalWWQglG7BQ0BqNBtatW6daxTFmwmAorZYVK1Zg3bp1KJVK2LNnDwDg4YcfRk9PDy688EKsW7dONXyJRCKqe1N3d7e6dz1FKxmdeiWm3IdKCoBSDvp+hKzfMFgezFNzATZ4YRqNqUZaBwwsUjnIl50KIhaLAbCv4sT/pQty5MgRTExM4Fe/+hXS6TSSySTOPfdcW/MUHpvJZFSNw9TUFAqFAiqViuqdwOvIGgSpLFigND09rRigrAHhgjiMLfC6mzdvxrp161TQtV6v49lnn1VpVZl1YEaFlanyh8FF3otefKYHbpdiluo0akOfdg+jHFxAchgajYaiJHM2lm3TuJ0CL6sRaS3oDEpgIf3Wbs8vylur1dTiM2ecccYiqrXs0xgIBBCLxZDL5TA1NaVIW3qhFn+44pW8HtOZTMW22/NL7sViMZTLZbVIbyKRQDQaxZEjRxQn4rXXXkOtVlOxEwqrXLNDF3qCf8s+GbRqeKxsp8d70RUEMF+VKnkQBsuDUQ4uwBeavP9QKKSYf4FAQCkB2fFZ+tI002UmQWcIym3SVE6n0+jr6wOw0HWKVgqwUDJOYWm351e5AuxrefI6ABCPx1XnJ87ijFGwVRyVWa1WU4qmVqthcnISwWAQnZ2dyrrw+/0ol8vqWUkOgh5TkIIriU46vVyuWSEVhjyHziWR1pjB8mGUgwvwpaUwVatVleKTrgWthEajoUxbuQ4lSUnyJZZ+M4+V//M6kjmo++EAFJU7k8koQed1dOp2LBZDR0eHqrSkYGWzWWSzWezfvx/NZhOZTAapVEoxJEmvrlQqGB0dVTN1JBKxZTSk0tMZkTLFKlf9ktaAE5tUWly6K8Ft8toGy4dRDi6gB71INpKUZwYiZSEWZ1vO4MFg0LFvgxR6gkJBAXJiA0oh4ezJbtG0LPRW+LIwKRqN2khcdB9Ir6ZiYodrFnE1Gg3FpuRKU4cOHcKaNWtUL0tpOejpR1oMVBCMj8h4iKzM1AOYS3EhJBPVYPkwysEFpL9MwWIvg2g0qgJvDFwWi0Vb5yifb2EJOqcIu/SxKTC8Ti6Xw+7du3HWWWepxWJk1B+Aim+QdpxIJFSwsFqtKmantIDYSm52dhZzc3Oq3V2hUEAikVB1FieeeCI2btyIQqGAqakp1SOi0Whg8+bNiMfjOHz4ML7//e9j79692LJlC7Zu3Yq1a9cueo6y9kE25JWulLQ0dKajDK7K74YKQzauMVg+jHJwAek7SzPX45lv4sJUHWd4NnDhC0uOhPSZ9aCkPCcDl8C8Enr44Yfx/PPP4/jjj8eWLVuQSqXU9aTfLlN/8Xhc/S3XlyS5KRqN4sCBA5iYmFDuDBu/RiIRRcaqVCqwLEt1laKbFI/HccYZZ6Cnpwc///nPVeryV7/6FV555RVcc8016O/vt2UkJK9CErZ8Pp+txbxUnDIOQZdEWlB8XizZ1uMaBscOoxxcQJruupKo1+uq9TwRCoUW+cdMc+rsP4JWiVwjg6jX6zhw4AAOHDiA3bt3Y8OGDTjhhBOwevVqBINBG4OQ45Vl1wBUPCQcDiOVSqG3txfJZBLf+c53MDQ0BAC24igGPXfv3o2Ojg5kMhmV+vR4PFi/fj2OO+44BINBtdju+eefryjmyWTSFjeQAiub5UpmqB6c1JWy/J/HMz0LQFV4GrfCHYxycAGd4y8JRe12G+VyWRU5ySIi/u3z+WxNVAndXdGj7ZIFSMtiamoK+XweTz/9NHp7e3HyySdj69atiMfjtoYoMmgpWYaSUbhq1Spceuml+OY3v4mZmRnFSZDsxVarpTpN0wrKZDI455xzkMlk0Gg00NXVpapGyfKU98F7k5aNVGQ8L5+PTvt2Ugw8VpKepHI1WD6McnAByXMAFoSZL2ytVlPBPX3WB+aFZGpqyhZU1N0KaZXItKS8DoWdMYmxsTGMj4/jySefRGdnJ+LxOOLxONLptEpXshms7o/z/IODgzj//PPxP//zP2i355uzVKtV9ZtVqJVKBel0GpZlYceOHdi2bRuAeSupr68Pw8PDmJiYUEVZS83eelCV9yv7Yuj769Ya4yZ8JnKtELonBsuHUQ4uIV9mWezEoBr7OnAfme9vt9vI5/Nqu86DkIqH1GsZ6ZcBO1m8BSwE+dgabnp6Gq+99poan9frRSKRQFdXF7q6utDX16fISpzRzzvvPADAI488Yrtfpinj8Ti8Xq8quDrllFPU2NiCLp1OK8GU9yifAV0A2WlaKlP9mSy1jdYRy8rpSpAWzkyHwfJglIML6AIpzX5aA2zrDtgXg+VLTXq0tBhovuvEJxloc1IMwAL5KRaL2awCSQqS5y6Xyzh06BDi8Tj6+vpUYJOWwfnnn49IJIJf/OIXmJ2dVZkPrlAVjUaxadMmnHnmmWqmJtLpNPr7+1VDGT3FymfH8enkJWkdcJFf/i/jKTJ9KdvFUTmwPZ6BOxjl4AIsaZazLWAvJGJMgS+qnBn5mYSejqMQSTdCh8yAsFxaNosB7FRpnXXJrEChUMCrr76K/v5+tcZDMBjE6aefjsHBQfzv//4vhoaGFMErGAyio6MDW7dutbXSJ0ciEAigv78fxWLR1meSz0BXJvL+JAeCn+nujyRFyWIyp9W1ZBm3wfJgnppLyI7IgL0rMl9KWaBEWNb8YrW6Dy5nee4nU5rSwpDCzoIm9lCQDEGdMMQyb8Ln86G7uxsrVqxAIBBALpfDxMSEjVfR1dWFSy+9FMcffzwymQx6enqwceNGnHTSScp14CI8rOqs1+uqr+STTz6JoaEhWy8LqUR5H7IkW2Yv+JvBVd3lkM1l5f1KN4pMVIPlwVgOLqE3e5XCzDhBvV5XDEEJr9eLVCqF6enpRQVFMt5AtiBnRQq2TPmxtwIFTW/OKvkSwALZyOv1IpPJoLu72xbdn52dhcfjQV9fn62J62mnnYbBwUFlPUjznYJLijjPt2LFCmQyGQwNDSEajWLFihWOaVt5P5IEpVtMMoArhZ/3I9moHBOb1xgsH0Y5uAAtg6VMfm7njCpbvAFAMpnEwMAAvF4vcrmcraxbCoacBSXxh4IUDAYVXVsWYfF/KgL67NLs9vv96OnpUfRiCpjHM79y18zMDLLZrMoadHR0YGBgAI1GA9PT0/B4PIr+LftVSE4Fsx0nnHCCalgDLBSW8W9p3XCbfNa8b6fP+Dz4mfybwUiTynQHoxxcgApBtqKXwiVf+Gq1qmZg/kSjUaxcuVK1tp+YmECxWHQ0mWUxEiPwMrYgLQEKih6/4AI5Upii0ajKpnBc0ldnD0u9zJqxDZkhoODq5r9lWYo2LpvC0roC7FkHnkNmLZzcLxkz4Zglz4Tnp5IynaDcwSgHF6AgkWPAl19aFBQQcgRkEI5Cxa5IiUQCQ0NDyqTXU3TAghCRfMT4gVylWo5Nd3l0HgEFXC9UkjNxqVSyuUW0DkgP14OK8ljZ3k1aM1RGMrbCa0srTAZ6dYtCErfkNRgQlS6HrnwMjh1GObiEFALp1+v+Lwuy0um0LQVHszydTiMWiyGRSOCFF15ALpcDYOdRMFinm8p6f0TpHkiLRudROM26HLO0fkqlEtLptJqFQ6GQGjfPRTA2AUCNT8YSuI9uEcgx01Ki4pX3Jf+WjW0k6QmAWmSI5zGZCvcwysEFnHgHUvAlNZhpTRY38Xi5AIzX60VfXx+CwSBeeuklHD58WPEK9DQfMwKSO8BrOvU44G8Zwaeg828KtdyPAinb33Eccr1MXosuj940V45RWg1OCksWWUlrRd9PulP6tflsmfWQ1GyD5cEoBxeQJjvJRXIm1IWDpCO6A9I3lv5yd3c3YrEYstksDhw4oDo4OeX4ZWNaaZoDsFkWPIYzfjQaRTKZVLUXstmKtFSk1UFLQ1owMoAoW7pJpbnUM+Nzkp9ROej76X/Le5Xl6nRD9FQw40IGy4d5ai6gC6T0baWAAQuC3Wg0UCgU1MKyUrikac8l7zOZDPbu3YtcLqcsBWkFVCoVmzDptRn6+SORCFKpFGKxmOolIf186W5IxcIUJYlMMhtA0N+XCpHxF17f6XlRkcoYi1QyTkpRnkMqFQYeqaxlR20Tc3AHoxxcQCoHKgH5EkpWHl/iZrOpeiyy87RO3JFKor+/H7FYDPv378fBgwdRq9VsfAV2X5INVPVgH68fCASQTqeRSCRsTWek68Hx6CXOJBHxOCoQaSU4bZNcA8Bef8LYglSo0qLQA516kJKBWGndyDgHqzqpeIxycAejHFxAWgp8yfUYhD77UXi4eAwbzOqdinic3+9HR0cHTj75ZASDQbz44otqfU0KSalUQjQaRSQSUccCC+3VCFZnOikSqSjkvemBU0nAYrdrKdByJuf4nZidHB8A27OT45fPWf+M5/Z6vTaLSioeXoNxEVN45Q5GObiAbI2u+9oyxSmtB2AhoEghZ/R/qTw8+Qjbt29HKBTC3r171bGkZ1cqFbXGBK8BzCuQUCiEVCqFTCajaiAoRDy/HrSUgUOdQyAVnRyznnbUswRS4ch9pLvhpEQkpKVD6MfKVcA9Ho+ibBu4g1EOLiCDcnrwTY8B6NkCWZPB38wccH+nzzdt2gTLsvD888+jUCiooOTc3JxSDlLQQ6EQstksksmkrV2avI6MK8jfUhBlWpF+vSRG8bdUHPqMrysG+VxkAFEqJ/3Zyf8luUmPteiMTUOCcg+jHFxCmrGyQApYiN5LMhR/630eSbFmkNDJ5wbmO0Mfd9xx8Pv9ePrpp9Uq3ewQHYvFlPnP1m+pVEqVbwOwWQJSIUiFoSsL1ju0Wi3bylHcl70wZXWmruBkwFG6IDI+occw9KCtnt3gNsZzGBuRy+PxvkzZtjsY5eACMqrPv/XIuJyVpX/PABmLgWS0XboHutnObevWrUOhUMD+/fvh8/mQyWTQ19eH7u5u1eGJdGk9paq3WpP9E5xiJCQZsSGuTqUmp4CkKCm0UgnKz/QsBs+jb9PdAZmpkOOXQVjGFnRCmoE7GOXgArKkWJ/l9ZQgYG8CI4VQrsVQLpdRq9UU1VoqHwoaZ9gNGzYgHA6ju7sb2WzWloEgpFDKbIJUEnow1emH52LzGilsvA/JkZD3qLsNVAw8l1QO0tqQioSC7+T+AFBBSfl8ZODVxBzcwygHF5Czn3wJ5ToU/M3ZmWa5TBXKtSABe+9IKQBSwH2++SXut2/fbivo0v1yeZyT0NEiqNVqyqXh/TjFC9igRq7S5STUurLUx8eSbN6vbrHokGOQ+/H6cmVzPstarWZLIZuYgzsY5eACulkrZ1mZQtPTgoCdI6Hn4PWZltfijC2bmsj0oj4unkt2fuLn/EwKD9eo0DMIuiVSrVbVftLM575OAVrduuKaojJO43Q9+Qx0libHL90iWmEM1NZqNWWBGZ6DOxjl4AIy4AhA1UgAi815PVPBWYxRdKegGwUuEAggHo/bgn2EXChHdw2kwpLWixQU6TKw85NT+bW0PlhhKrMfUvFJyGAjIcdFBaF31OJ+usXBMUlrRFasyka1tOy8Xq/hOLwJGOXgAk6zPUupKcS6ucvjOPPJ1a7kCy+PTyaTCIfDNoEllgrccT+9ElIqD/6vr1wN2DkMekCT3IpIJLKI16AL+FKfSRdBbuP+csxO55Pug3SP+Lzl9yPjPwbLh1EOLqDHAoDF7D5pVchj9P34YvMzClQymUQ0GrUJupzt9dlaDwRyfydSkzwHx8PFc5eKXxBsXaczJCX0a8oZXcYQ5H1IhSEVg3Td9FiPDDwC9uIx2e7eKAd3MMrBBeTMppdVSyGVDU/0mVDPXMjzptNpZDIZ1XUJWBA4LtTiVNMhXRkna0RmEGjByNRqtVoFgEXxB5ndqNfrqNfralzAgmDrwu60II1UDE7P02lfef9SqelNX9iOj0FIrglq4A5GObgAX2TdJ+fLKmcz+vtSYHXh4P+BQACpVAo9PT2K1OTxzDP95CpOrM+wLEstRKv777rAyZlZdwk4JnbLZu2H7sYA88qoVCohHA6rLlF6BkGeW96ntB70gisJJ8XBLImsXdHvlUFbr9eriFtmOTz3MMrBBejn8m8nwXw9d4NCIc/j9XqRTqfR19e3qOejJDSx4pDXlTO1E8GK1+S4+FvGIGTXawo/g5O6FcK0ZqlUsjXOdbJQaOnIa+vPRHdN5HmoAOjCSOvBKcbB46k4yZY02Qp3MMrBBSiQeiReN4Pl6tp6BF4qCJ/Ph87OTnR0dKjl7mktyOXw5PV5nO5SAPbGtHpsQVoLvL50L4B5ZVIoFODxeGzBR+5nWfMt5EKhEJLJ5JIugUynyh+6XE5xBj1DQauBq3zLscpUqO7WUImS92CwfBjl4AJydgLsbdMpeLJLk6zQpBCw/0AgEEBvb68qkJIuCLAw60uXRTZX0QN6PCchK0j1sctUpB5HaTabyjpg4xS5T6PRwOzsrFo3g5/pykrO+BRqJ1o170Eey+MleYzn0YOcTpYLMxgmIOkORjm4AF84WXQlswoAbNF8wilo2dnZqZrPUvhllkFaJ8wS6GQlwK4cWKNRqVRsgVHpTkgrhmPiNXlfbI4bCoVsy/rJGblYLC5qcc/z6UrAyYWQFZ1OfAlJeuI6GXy+fD56DAiA4m0Yt8I9jHJwAb27spxVdY6ATMHppm84HFYWA9NyVDJ6UJMZBl34pGDJFajIDiRFmoFLPQPB8+jmOq2LarWKcrmMZDK5qINTs9lEpVJRNSFOLo68xlJxGEIqGKl0CT4bWg16vIfP+/WUtsGxwygHF+ALKTMT8sWWQu50rGVZCIVCyGQyCIVCtpWrANj6HzIjQegvvBQKWhl66/pgMIharYZarbZoFnVSaNxOIS0Wi2oxHQon74XKQxZ/SSXhZOG8XjBRWlvSbZL3qtewyO9EZ286pZENjg1GObiArGmQkXw548mYA2CPS7CnYywWQzQaVWQnGSvg7Mg1GCgM0l2RfAU9wu/xeGxKghkQ9o+Q1o5u/dDS4f1JBSHbvnN8pVIJsVjMxsrUIRUFLRQZX9EVhlR6Tu4GW9FLZSRjEvyOJHPSYHkwysEFaK7rQTXdepACwd+RSASxWAyhUAiRSATRaBR+v1/59XQDeKx0B2SWAsCiWggpYDIAymNZDk4iky6Asu5CzxjUajUUCgWk02mVCaCLUalUUC6Xl2yqomdL+Kzk+AjpesiuU7rFQUXJupalGJFScRosD0Y5uIBML8qXW1YJAvYmrrQYuP4k3QnJRZAdjNgfUl5P1mXwf1lPwP10wZM/TAv6/X7U63XbOPXAoJ6h4OI8XOOT1km73UalUlFxCacxSKF3cit0Jaq7FBIyXSz5Hnq2Q//bYHkwysEFlmIYAouXvec+oVBIrRzl8/kUf4BuiAwu6su56RkKPVDH63Jf/q+b2/whAzIcDqs+DQAWxQpkBoNgO3wqJn5OqrLkNejH8ho6y1GPc8hnKy0Bqayka0JFyV4Rsl7FwD3ME3QJvRhKJ0bJl97v9yORSCjBkSXY0hrgD10JfWl73fTXzXE58+vEIF1BSGukUqnYlqqnApFcDm5vNpsoFou2JfLoCpFWrbtTS7kNkuHplIWR5+Fx7LZN6KXvUqnKYLHB8mEa7LmAfOGdshRSGLze+fbyNLcZdNSzHVKRyMAeZ2mel26BvmKVpDvLMcogYKPRQK1Ws8Uc/H6/YmXqZCj9PPy/UqmgUqnY3A/yHpZSXjqWSm1Kl0HfBsBxXU8Zj6HVJi0xE3NwB49l1KqBgYEDjOVgYGDgCKMcDAwMHGGUg4GBgSOMcjAwMHCEUQ4GBgaOMMrBwMDAEUY5GBgYOMIoBwMDA0cY5WBgYOCI/w+/DWDyoMFk9gAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_ft)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:12.270208149Z",
     "start_time": "2023-06-19T09:44:11.647734599Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "# upl"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:12.274374162Z",
     "start_time": "2023-06-19T09:44:12.272719711Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# here I want to put those bees trough grayscale, put the same image to all three channels and see how it impacts the efficiency\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:12.297164318Z",
     "start_time": "2023-06-19T09:44:12.276167862Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (23): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (24): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (25): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (26): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (27): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (28): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (29): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (30): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (31): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (32): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (33): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (34): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (35): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model_152 = models.resnet152(weights='IMAGENET1K_V1')\n",
    "num_ftrs = model_152.fc.in_features\n",
    "\n",
    "print(model_152)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:13.096310243Z",
     "start_time": "2023-06-19T09:44:12.295332848Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (3): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (4): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (5): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (6): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (7): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (3): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (4): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (5): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (6): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (7): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (8): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (9): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (10): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (11): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (12): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (13): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (14): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (15): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (16): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (17): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (18): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (19): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (20): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (21): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (22): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (23): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (24): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (25): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (26): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (27): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (28): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (29): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (30): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (31): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (32): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (33): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (34): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (35): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n    (2): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): Linear(in_features=2048, out_features=2, bias=True)\n)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_152.fc = nn.Linear(num_ftrs, 2)\n",
    "model_152.to(device)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:44:13.164219989Z",
     "start_time": "2023-06-19T09:44:13.097585538Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "----------\n",
      "train Loss: 0.5325 Acc: 0.7213\n",
      "val Loss: 0.2718 Acc: 0.8954\n",
      "\n",
      "Epoch 2/30\n",
      "----------\n",
      "train Loss: 0.4979 Acc: 0.8074\n",
      "val Loss: 0.4812 Acc: 0.8235\n",
      "\n",
      "Epoch 3/30\n",
      "----------\n",
      "train Loss: 0.4631 Acc: 0.7992\n",
      "val Loss: 0.8017 Acc: 0.6863\n",
      "\n",
      "Epoch 4/30\n",
      "----------\n",
      "train Loss: 0.6532 Acc: 0.7459\n",
      "val Loss: 0.4402 Acc: 0.8497\n",
      "\n",
      "Epoch 5/30\n",
      "----------\n",
      "train Loss: 0.6861 Acc: 0.7623\n",
      "val Loss: 0.3170 Acc: 0.8889\n",
      "\n",
      "Epoch 6/30\n",
      "----------\n",
      "train Loss: 0.4725 Acc: 0.8156\n",
      "val Loss: 0.3029 Acc: 0.8824\n",
      "\n",
      "Epoch 7/30\n",
      "----------\n",
      "train Loss: 0.4059 Acc: 0.8484\n",
      "val Loss: 0.3183 Acc: 0.8758\n",
      "\n",
      "Epoch 8/30\n",
      "----------\n",
      "train Loss: 0.4011 Acc: 0.8443\n",
      "val Loss: 0.2538 Acc: 0.9150\n",
      "\n",
      "Epoch 9/30\n",
      "----------\n",
      "train Loss: 0.4288 Acc: 0.7910\n",
      "val Loss: 0.2569 Acc: 0.9020\n",
      "\n",
      "Epoch 10/30\n",
      "----------\n",
      "train Loss: 0.3386 Acc: 0.8525\n",
      "val Loss: 0.2287 Acc: 0.9150\n",
      "\n",
      "Epoch 11/30\n",
      "----------\n",
      "train Loss: 0.2547 Acc: 0.8852\n",
      "val Loss: 0.2124 Acc: 0.9281\n",
      "\n",
      "Epoch 12/30\n",
      "----------\n",
      "train Loss: 0.3699 Acc: 0.8279\n",
      "val Loss: 0.1966 Acc: 0.9346\n",
      "\n",
      "Epoch 13/30\n",
      "----------\n",
      "train Loss: 0.3557 Acc: 0.8361\n",
      "val Loss: 0.2018 Acc: 0.9346\n",
      "\n",
      "Epoch 14/30\n",
      "----------\n",
      "train Loss: 0.2469 Acc: 0.9016\n",
      "val Loss: 0.2355 Acc: 0.9150\n",
      "\n",
      "Epoch 15/30\n",
      "----------\n",
      "train Loss: 0.2690 Acc: 0.8730\n",
      "val Loss: 0.2120 Acc: 0.9216\n",
      "\n",
      "Epoch 16/30\n",
      "----------\n",
      "train Loss: 0.2528 Acc: 0.9057\n",
      "val Loss: 0.1918 Acc: 0.9281\n",
      "\n",
      "Epoch 17/30\n",
      "----------\n",
      "train Loss: 0.2782 Acc: 0.8689\n",
      "val Loss: 0.2106 Acc: 0.9216\n",
      "\n",
      "Epoch 18/30\n",
      "----------\n",
      "train Loss: 0.3491 Acc: 0.8320\n",
      "val Loss: 0.2010 Acc: 0.9477\n",
      "\n",
      "Epoch 19/30\n",
      "----------\n",
      "train Loss: 0.2135 Acc: 0.9139\n",
      "val Loss: 0.2164 Acc: 0.9216\n",
      "\n",
      "Epoch 20/30\n",
      "----------\n",
      "train Loss: 0.2053 Acc: 0.8934\n",
      "val Loss: 0.2054 Acc: 0.9281\n",
      "\n",
      "Epoch 21/30\n",
      "----------\n",
      "train Loss: 0.2040 Acc: 0.9303\n",
      "val Loss: 0.1972 Acc: 0.9412\n",
      "\n",
      "Epoch 22/30\n",
      "----------\n",
      "train Loss: 0.2522 Acc: 0.9180\n",
      "val Loss: 0.2092 Acc: 0.9281\n",
      "\n",
      "Epoch 23/30\n",
      "----------\n",
      "train Loss: 0.2632 Acc: 0.8934\n",
      "val Loss: 0.1996 Acc: 0.9346\n",
      "\n",
      "Epoch 24/30\n",
      "----------\n",
      "train Loss: 0.1943 Acc: 0.9303\n",
      "val Loss: 0.2143 Acc: 0.9216\n",
      "\n",
      "Epoch 25/30\n",
      "----------\n",
      "train Loss: 0.2557 Acc: 0.8811\n",
      "val Loss: 0.2195 Acc: 0.9150\n",
      "\n",
      "Epoch 26/30\n",
      "----------\n",
      "train Loss: 0.2390 Acc: 0.8975\n",
      "val Loss: 0.1997 Acc: 0.9216\n",
      "\n",
      "Epoch 27/30\n",
      "----------\n",
      "train Loss: 0.2306 Acc: 0.9057\n",
      "val Loss: 0.2025 Acc: 0.9346\n",
      "\n",
      "Epoch 28/30\n",
      "----------\n",
      "train Loss: 0.2759 Acc: 0.9016\n",
      "val Loss: 0.2451 Acc: 0.9085\n",
      "\n",
      "Epoch 29/30\n",
      "----------\n",
      "train Loss: 0.2751 Acc: 0.8811\n",
      "val Loss: 0.2037 Acc: 0.9281\n",
      "\n",
      "Epoch 30/30\n",
      "----------\n",
      "train Loss: 0.2303 Acc: 0.9057\n",
      "val Loss: 0.2064 Acc: 0.9281\n",
      "\n",
      "Training complete in 3m 23s\n",
      "Best val Acc: 0.947712\n"
     ]
    }
   ],
   "source": [
    "# observe that all parameters are being optimized (what does it mean?)\n",
    "optimizer_152 = optim.SGD(model_152.parameters(),lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 10 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_152, step_size=7, gamma=0.1)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "model_152 = train_model(model_152, criterion, optimizer_152, exp_lr_scheduler,\n",
    "                       num_epochs=30)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:47:36.205998624Z",
     "start_time": "2023-06-19T09:44:13.166606532Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "torch.save(model_ft, 'dummy_data/hymenoptera_data/ResNet18_bees_ants_grayscale')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:53:23.390023124Z",
     "start_time": "2023-06-19T09:53:23.352279660Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "model_loaded = torch.load('dummy_data/hymenoptera_data/ResNet18_bees_ants_grayscale')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:54:20.470600958Z",
     "start_time": "2023-06-19T09:54:20.342534294Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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nNps5qVTKnThxgrt8+TLn8Xhe0e33eDyv2Yl++Xv+5m/+JudwODipVModOnSI+8Y3vvGKTjbr9v/Jn/zJK94DAPfEE0/c9p6//Mu/zJlMJvquu7u73Le//W3uoYce4hwOBycWizmz2cw9/PDD3Isvvnjb+x0+fJizWq13PHb2b6bT6TiVSsW97W1v41ZWVl7194hGo9z73/9+zmq1ciKRiLPb7dxjjz1227n0la98hZucnOREItErvtMXvvAFbmpqipNKpdz09DT3d3/3d6/a7f+rv/orbmJigpNIJNzIyAj3R3/0R9znPvc5+g0YXq3b/wu/8AuveN6Pwl4/50fVxqt9/ic/+UnO5/NxAoGAzvuNjQ3uPe95Dzc6OsrJZDJOo9Fwx44d4z7/+c/f8RhfDh7H7Z3p8JGPfAQf/ehHkc1maV47xL9/VKtV6PV6fPKTn3zdE5Qh/u3h3wW3f4h/WbzwwgtwOBz4wAc+cLcPZYh/RgyLf4g74pFHHkEoFIJYLL7bhzLEPyN+rGX/EEMM8e8Hwzv/EEO8QTEs/iGGeINiWPxDDPEGxbD4hxjiDYp/VSefexU7OztYX1/H5cuXkc/nYTQaYTQaYbPZIBAISHk3GAywtbWFfD4Pr9eLmZkZaDQadLtdSCQS9Pt9tNttPPPMM8hmszh9+jRMJhO++MUv4uDBgzh9+jSq1Sp4PB5MJhMkEgmCwSCSyST6/T6EQiGazSay2SzMZjPkcjnUajUajQbEYjHC4TAajQbC4TBOnz4NjUaD7e1tjI+PY25uDs1mE7lcDjdv3kQ2m4Xb7YbZbCbq7bPPPotqtYp+v493vetdmJmZgUwmg1gsRqfTweLiItRqNaxWK0qlEm7evAm5XA6O4/Dss89Co9HgLW95C1QqFdbX1xGPx9HpdFCpVFAsFvETP/ETsFgs2NzcxPj4OGKxGDqdDhqNBjH6VCoVeUK0222kUilks1lsbm5CIBDA7/dja2sLu7u7yGazOHfuHN785jdDJpOBx+NhMBigWCy+Qk06xCsxLP49wO12w263Y2JiAt/73vdQq9XQbrexsrKCRqOB++67Dz6fD61WC4lEAru7u8jlcojFYrBardBqtWg2m9jc3IRYLEY0GoVarYZEIoFarcbDDz8MjuMwGAygVqsRDAYRDocxNTUFnU4HiUSCCxcuQCaTweFwgMfjgc/nw2azgcfjQSAQYDAY0MXIZDJhdnYW6XQaYrEYyWQS+Xwea2trGB8fh0QigcFgwKlTpyCVSrGwsIByuQyVSgU+nw8+nw+NRoNerwc+nw+xWEyvuXHjBvr9PorFIlqtFpaWljA2Nga5XA6j0Yhut4tWq4VUKoVOp0NqzWQyCbPZjHa7DblcDp/PRxe3paUlSCQSaDQayGQy9Ho9pFIpNBoNNJtNGI1G2O12vPDCC1haWoLb7YbRaMTCwgIKhQJqtRrkcjlarRa2trbQarWGxb8HDIt/D9jY2MD4+DhcLhdGRkZw7do1ZLNZNJtNNBoN5HI5OJ1O1Ot1ct4Bbqn9arUaFAoFdDod/H4/rl69ilqtBp1OB7lcDj6fjwMHDqDVakEkEiGZTEIgEKDRaODixYuYmpqC2+2GSCTC1tYWRCIR2u02Wq0WhEIhZDIZ5HI5isUiBoMBpFIpDh06BJvNBo7j0O/3kU6ncf36dQSDQYyNjcHn86FcLqNeryMUCkEikcBsNqNQKKBaraLdbmNjYwM8Hg8KhQKDwQDA/38nXlpagtfrRb1eRz6fx/T0NM6cOQMej4d4PA6fzwebzQalUon9+/ejXq/T8SwtLaHX60EsFoPP5yOdTsNut0MikeDGjRu4fv06QqEQyuUyTpw4AalUShe1SqUCuVwOi8WCWq0GpVKJSqWCF198ETMzM6hUKmg0Gq+qqxjilRgW/x7wzW9+E29729ugUCiQyWSQTCbRarWgUCjQ6XSQTCYxOjoK4Jb3H/Ml3NraoruvXC6nO/TMzAx6vR70ej1sNhtkMhlKpRLq9Tr4fD5arRZqtRqy2Sx4PB6kUimsViucTifC4TCi0Siq1Sodj8lkQq1WA5/Px8TEBLrdLqnlMpkM2u02HA4HOI6DTqeDVquFRCLB+fPnkclk0Ov1oNFowHEcFbhcLqcidTgcaDabmJ+fx+joKGZnZyGVSpHNZqHT6TA1NQUej4dCoYBisQiVSgWZTIZcLodgMAipVAqj0YhisQiDwYBSqYRLly7RakomkyGTyUAsFiMSiaDVasHv9yObzWJkZAQKhQLNZhOTk5OYnJyEyWTC+fPnkc1mYbPZsLW1RcesVCpf1UhliFdi+CvtAYlEAleuXCGGm8FgQCwWg06nw+joKMrlMpaXl6HVasFxHKrVKprNJkZGRiAWixGLxdBsNpHJZNDpdDA+Po5yuQyRSASlUolGo4FWq4VyuQyhUAidTofr169DrVZDJpMhkUggl8vhvvvug0QiQbvdhkqlws7ODjY3N1Gv13HmzBn4fD7qFaRSKfD5fKjVaojFYsTjcbpo9Ho9LC0tUfFGIhGYTCZIpVJUq1WkUimk02nUajVUKhUcPXoUarUacrkcnU4HBw8eBMdxkEql4DgOu7u7EAqFkEgkKBQKkMvl8Hq9pNY0GAxQq9VwuVwwmUyIRCLI5/OIx+Pw+/3w+XzIZrOIx+MoFos4deoURkZGoFQqMRgMqJ/idrvRbreRy+WQy+XQ7XYhFAoxOTmJdDoNh8MBh8OBSqVyl8+YewPD4t8DrFYrFAoFTCYTstksBAIBxGIxRkdHqaH35JNPQiwWQ6/Xg8/nQ6lUYmRkBBqNBrlcDi+88AKazSY6nQ7tpVlPoFKpYG1tDZVKBTabDRqNBl6vF2NjY1Cr1UilUhCLxeRfr9PpoNFoaE9crVZRLpfRaDRQq9WQz+dRKBQglUrRbDah1WohFArB5/Px/e9/H+VyGalUChMTE6hUKlAoFJicnKT+AdvTh0IhcByHVquFo0ePwu/3Y3t7G0899RTK5TI0Gg0efvhh6HQ6NJtN1Go1NJtNLC0twWAwYGRkBOfPn4fRaIRWq0Wv10M4HEYwGMTo6ChKpRIKhQLZfXk8HsRiMXg8HkxMTEAsFqNWq6HX60Gn06FarZJmXiAQwGg0YmZmBlqtFp1OB6FQCEKhEMvLy3j00Ufv7klzD2BY/HvA7OwstFot1Go17HY7PB4PNZoymQzUajX0ej2kUilEIhHMZjO63S5yuRz9/dChQ2i329jc3EQ2m0W/30c+n4dcLke/30e324VGo4FEIkGxWIRUKkU8HgfHcbDb7SgUChCLxWi32xAIBGi321AoFOj3++A4Dt1uF5VKBbu7u6jVahCJRAiHw2i1WnjkkUdgt9thNptx8eJFXL9+HTabDSKRCFKpFBaLhd4zm80il8uBx+NhenoapVIJjUYD29vbmJqagsFgoA68SqWCXC6HSqWCWCyGUCjE6Ogo+v0+NSZLpRKi0Sj5NuTzeQwGA1QqFbRaLRSLRbTbbUxOTsJsNsNmsyGTycBisUAsFqNUKmEwGKBarUIgEMBqtUIkEqFYLNK0g8/nw2g0olarYWtr6xVuPkO8OobFvwfIZDJIJBLw+Xzo9Xp0Oh1IpVKsr69jdXUVJpMJIpEItVoNMzMzOHPmDNrtNu31O50OTCYTKpUKLBYL6vU6gsEgZDIZwuEwlEolBAIB2YN1u11IpVIoFArUajUaX504cQKpVAqXLl2CVCqFTCZDu92mvkCj0UChUIBGo0EikaCRYzQahc1mI8NIu90Oh8OBK1euQKlUwuFwIBKJQCKRYGxsDIVCAclkEqdPn0Y8HketVgOPx8Pa2hrS6TQ0Gg38fj9kMhmefvppnD17FjKZDNvb2wgGg1CpVMhms6jX6xCJROj1elheXkapVEKtVkMkEkGtVoPNZqN+hlwuR6lUov6DSqXC6uoqisUi+Hw+rFYr9Ho9hEIhFhcXqeeyvb0NpVIJPp8Pl8uFcDiMZDJ5N0+XewbD4t8DOp0OcrkcrFYr3aE7nQ78fj8qlQo5tQ4GA0gkEmi1Wrqj5/N51Go1JBIJsrhqNpvodrvw+/2oVquQyWQYDAYoFAqQyWQoFou4du0a3G43RkZGEI/HMRgMwHEcMpkMNBoNlEolAoEABoMBXC4X7HY7AoEAGo0Gde87nQ7i8Th6vR5qtRo4jgPHcSgUClAoFDhw4AAWFxexubkJkUiEmZkZ+P1+5PN5yGQy2mNnMhly4e10OhRKIZPJUCgUEA6HIZVKEYvFMBgMoNFosLu7i52dHUgkEjQaDbz00ksQi8U0BVCr1Wi1WrBYLDTBCAaDSCQSMBqNyGQyyOVyNGmRy+WoVCq4evUqKpUKZDIZFAoFut0ukskkhEIhTCYTlEol5HL53T5l7gkMi38PkEqlWF1dJaKOVCqlWbvdbofRaES1WsXIyAgsFguSySRMJhN4PB5WVlbA4/GwubmJUqkEi8WCUqkEq9VKQSbNZhOVSgX5fB46nY6WxqFQCLlcDoPBAHw+H8888wz6/T6azSaEQiGMRiPEYjHcbjcSiQSSySRsNhv6/T74fD6EQiHkcjmEQiG+853voN/v02QgFosRH0EsFkOhUNDyfWRkBDMzM3C73Wg2m5BIJHQBYXdxq9UKr9dLRKGtrS1otVoat8nlctTrdUocGh8fx87ODvh8Pt71rnchEAhgfn4e5XIZMzMzyOfzqFQq0Ol0sNvt0Gq1mJ2dBcdxGBkZAQA0Gg1oNBra+4vFYggEAnQ6HfB4PLRaLWg0GszNzd3dE+YewbD494BWqwUej4fV1VXyV2cjvl6vB61Wi0KhgEajAbPZjGaziYWFBYhEIly9ehXtdpu8/xUKBbRaLVwuF2KxGFKpFM3ye70eKpUK5ubmcP/992N3dxerq6uw2WxoNBrk/x4MBok8pNPpEIvF0G634fF4YDAYkMlkqOHW7/chEAiQTCaxu7tLf0+lUuDxePB6vdBoNGi1WtBqtcQQfPjhh4m9qFarsbq6CoFAgMnJSUilUojFYqRSKVQqFbTbbSwvL6NcLhNDUafTwefzwW63Qy6Xg8fjwe/3Q6FQYGRkBHK5nBqKdrsdPB4PcrmcehGMA+F0OiGRSFCtVlGpVKBUKiGTySCVSsHn32Knj46OIpPJ0Cjx/8U3742EYfHvAayhtLy8TA0qNvZrt9uIxWJE0Gk2mzAYDJicnKTwBolEglarRXtyi8UCjUZD7y+TySAQCNDr9dDv96HX6wEAlUqFDDt5PB6MRiN6vR4ikQg1xRQKBZLJJKRSKfbt24d2u43BYACRSETL8mg0irNnz+LYsWNYX19HMplErVbDsWPHcPbsWWxsbODy5cv43ve+h6NHj4LjOFQqFdTrdVy5cgVqtZq2D8eOHYPP50O/30epVEI+n8fOzg514FUqFQqFAqampjA7O0sMxU6nA4FAgI2NDWxubhKLsNVqQS6XU88km81Cr9fT7yOVShEIBKDX6yEQCHDz5k0MBgOiHXMch3q9jn6/Tyw/RrIa4rUxLP49oFAoEAGnVCqh2WzCZrMhlUpBqVSiWCyi0+lAp9Mhl8shGo3C4XDQiCscDiOVSqHf79Odl+112fI4Ho8jkUhALBaj2+2iUChQUysWi0GhUKDVatHce3d3l+bhjMlXKpVQLpfR7/cRj8fRbrchEolQr9chl8sxNTUFj8eDr33ta2g0GpiYmIBUKkWn0yF33Fqthmq1SgXq8XggEolQLpcxPz+P3d1dAKCiFovFcLlccLlcSCQSSKfTyOfzaLVaxDlg35PjOKjVarzwwgtQq9UIBAIQiURYXV2FwWBArVZDKBTCyMgIVCoVer0eZDIZkZ3q9ToajQZdaFqtFl002e8/GAz2FOwyxLD494RoNIpYLIZMJgOz2Qwej4dGo4HZ2VksLi6i0+mgWq2C4zharl68eBHVapUCFtvtNhqNBnq9Hl0sbDYbvF4v+Hw+rl27hng8DqlUCq1Wi1wuh3q9jlwuB6PRSOSfwWAAj8dDTMC1tTWKEnu5PoC91uFwoN/vIxQKYXR0FF6vl8ZqPp8PHMdBr9dDoVCA4zjcuHEDHMdhfn4eJ06coLurWq3GiRMnYDQaodfrYTKZ6GLGCDusgE+cOAGLxUJbHalUSmQmpVKJyclJiMVieDwe1Go1mM1mIkexXodSqaQpCustLC4uUnISAIhEIhp/chyHcrlMxzvEnTEs/j0gGo0iEomAx+NRth0jppjNZqyvryORSMDlcsHr9UKr1RIpSCaT4ciRIzAYDLh06RKKxSKazSbq9TrK5TKkUilFgGs0GhrHlctldLtdWK1W6HQ6iEQiutMyUU8ymQTHcfB4PGg0GnA6nWg2m8Q2ZKuEI0eOgM/no9lsYmdnB71eD4cPH6bth9frRafTwc2bN2nfzi5KrLPfbDYxGAzg9Xop8rzVatHsPp/Po1wuUx6AyWQigpBAIEC1WkWxWKQCHxkZgUwmQyQSgVarhUAggFAohM/nw+rqKi5duoTTp0+j2+2iVqtBrVbDYDAgEAhALBaT3oBddIVCIRqNBm3RhrgzhsW/B0ilUoyOjtIsms3Y8/k8stksgsEgANBSlTHlmOmlUqnE+Pg46vU6Ll26hGazCT6fj3a7jfn5eeTzeYyNjeHBBx9Ep9PBs88+C6FQSJHQjJZrsVigVqshEokgEAiwvb0NABAIBFheXkYwGITH40Gv18POzg4sFgtdoLa2thAOhzEYDCAQCJDJZJDNZqFQKCCRSCjjzeFwgM/ng8fj4cqVKwgGg3A6nVCr1chms5ieniZCEWPb1et1XLhwAU6nE4899hitJNgFstPpUOe/WCxifX0drVYLNpsN1WoVnU4HGo0GzWYTGxsb1B9gCkIej0fNVTZCZUzEbrdLZCc23Rgu+/eGYfHvATKZDFarlZb7HMdBoVCg1+shl8tBo9HQ3yuVCjQaDdrtNvr9PsRiMSqVCmq1GhqNBhQKBWQyGfR6PUU8WSwWrK6uQq1Ww+/3w+FwYGdnh+7cEokEAoEACoWCRlpGoxFOpxOZTAatVos09uVymYhIPB4PzWYT0WgUOzs76Ha70Ol0MJvNSKfT4DgORqMREomEFHo+n48eGxsbw+LiIi5evAi/3494PI65ubnbGIGMgqvVaolr32q10O/3SbnIcRxu3ryJXq9Hn9Nut6lhmM/nMTc3d9sWY2JiAmq1GpcvX8b29jYMBgOMRiMt+dmWgnEs+Hw+MRYVCsVdPmPuDQyLfw+wWCzUPGP7aSZ62draonFYs9mkO75CoaClOjP7SCQSUKlUJG7p9/vQarUYHx+Hx+PB9vY2isUiDhw4gGg0CpFIBK1WC7fbTWIVdpeMxWIwGAz03hKJBBzHodFoQKfTwWazIRgMIhQKQafTUVGo1WqajdvtdgSDQcjlcuzbtw+JRIJIRKwnkU6nkclksLq6itnZWQQCAWSzWczOztLsv9/vw+VyIZfL4dKlSxQ/XSwWoVaraUTHCtVqteLgwYO0ZRofH6emoFarJZ7EYDDA9PQ0vF4vdnd36W7P5/PR7/ep689xHD1Wq9UQDAZx5MiRu3zW/NvHsPj3AD6ff5thhEgkQigUQqlUQrFYpAuBTqeDxWKBTCZDpVKBVquFXq8nRhtr0k1MTBDzr9Pp4Nq1a0SG8fl80Ov18Pl82N3dRbVaRbfbhUqlIn67TqdDq9WCSqWC0WhEs9mkPXkul0M6naZ5uEqlIv8AdscXCoWwWq1oNBro9/sol8vY2dmBTCaDSCRCt9tFNBoFABLeiMVikukyYpBcLke326UegE6nA3Ar5ZgxH/v9PmKxGLrdLubm5lCpVFAqlSgJ2ev1QiwWY2FhAd1uF5FIBN1uFwcPHkSn0yE1IJuAsItcq9WCRCIhAhRbbYjF4n+22O1/7xgW/x6QyWSQSCTgdDqh1WrR7/fR6XQgl8shFotpDs7sqlgDjdlrtVot2sd6PB4yzPB6vaR0a7fbEAqFqNVqCIfD2N3dvW0vLBaLkUgkcOPGDXg8Hmg0GgiFQnAcR9sPRjRiXH+NRgODwYBcLkfqPaFQCLvdDrFYjOXlZRw6dAjXrl1DJBIhdmAwGITFYoFEIsHW1hbMZjP1GZrNJkqlEhQKBQwGAzweD+29GfdAIBCAz+djMBgQ+aher2MwGJANGRtbVioVaqRWq1UkEgnUajWaEKRSKahUKqjVapILNxoNiEQisjVjtGWRSIRWq0XknyFeG8Pi3wMCgQDNxKVSKRKJBJFvdDod3X3j8Tjt4w0GAyqVCp566ikYjUZYLBYYjUbIZDI6oRlvXqlUwmg0Ynt7G1tbW3A4HCgUCnA4HJBKpWi325BKpaRWK5fL4PF4UCqV6Ha74DgOLpcL29vbEAqF6Pf7iEajqNfrNAZjy27Wn2AXmkajQUVUq9UgFAqpbxEIBOB2u9FoNNBut8m/cGJiAkeOHCGxUzKZRDgchslkglAoRL1eh91ux+LiIlwu122rjnw+T34DTM8QCATw0EMPodFoIBKJQKVSIZ1O00oin8+TSchgMEC32yXfQT6fT8t+dhFgzcshXhvD4t8DZmZmYLVaoVKpcO3aNVQqFUgkEmxvb0Oj0RD7DgCx63w+H4RCIQlgJiYmyErLaDRiMBjQklgoFCKXyxGPntmDZTIZZDIZ4ulvbm4S2SWbzSKdTkMmk8FgMNCdtl6v4/DhwwiFQlCr1VAqlVQUwC1jEjaSYyrDZrMJqVSKwWCAVqsF4NYIzeFwYHd3l/T0pVIJWq0Wk5OT6Pf71M+4//77ifkoFAopitzj8ZAoyOPxQCqVolQqIZFIIBaL4R3veAdUKhXuv/9+9Pt9Ih71ej289NJL0Ol08Hq9RDNm7D1mV8aOgTES2W86LP69Ybg+2gMY/TSbzUImkxGtlgl8MpkMeDweXC4XPB4PnE4nOp0OWq0WJicncebMGWi1WjgcDmIBsq4/4/uzZhUT87CVRbPZJBWfxWJBp9NBJBIBcOtCwzj/bNXR7/dRr9fJ5otJfjudDslxmYpQLpfTd2B7ZpVKBY1GQ8txplUolUoYGRmB3+/HYDDAjRs3cOPGDTq+ffv20V2d4zgkk0kYDAa4XC4AwI0bN4iJJxKJYDAYwHEc/H4/RkdHabtjNpuRSqUQCAQQi8WIQ2GxWFAul1Gr1SAQCACA5vuDwQCDwQCdTgfdbhc8Hu/unCj3GIZ3/j2A7e1zuRykUikRdUZGRjA9PY0bN24gHA7D4/GQ351AIEAgEECr1UIsFiObrNHRUSQSCTL/GBsbI4MOjUaDcDgMkUgEo9EInU6HUCgEh8NBJz074fP5PEQiETqdDiQSCV2UhEIhut0uer0eQqEQXC4XWXa322088sgjsFqt6HQ62NnZIX49m6MHAgEIhUK4XC6IRCLodDpaSrMpRKfTgVarpSYgu4hUKhUsLS1Bq9UiEolg3759cLlc5AcQiUQwGAzI0XdhYQG9Xo/Gm/V6Hdvb2+A4DiqVCuPj4zAYDDQyZONOZmDCVipsZTQYDIi3MMSdMSz+PYD5y62srJCijanzer0esc9MJhMAoFgs0ok6GAxQLpdpJm6324mbr1ar4Xa7IZPJ0Ol0sL29jVqthlQqRfr8er0OjuPIzIKZfwwGA2xsbBApp9lsQi6Xk85doVBga2uLHHhLpRLa7TZCoRA0Gg0t0yORCOr1OnQ6HWq1Go3eGNGH2Wyvr6/jpZdeQjqdxvHjx2G321Gv1+m77u7uguM4BINBiEQi7N+/H5FIBPPz8zTau3nzJqrVKsbGxtBqtYjey0aabIwoEokwMTEBrVZL0ulIJEKkolarBY7jaKUDgDgCbOQ3xJ0xLP49oF6vUyBEPp+HVCrFzs4OLUMZ6YZ50ovFYpTLZZTLZeK0M25+sViESCSiO6tMJqPRGrvjBgIBCuqQSqXY3NxEt9uFWq0mu25W5G63G4cOHcL8/Dx6vR6ZbtRqNVgsFpoumEwmmM1mJBIJiEQi0vaXSiW667NG3ZEjR9BqtVCv18k9h9F/GVNxbW0NzWaTdAMrKysYHR2F0+mE0+mExWIBcCvwJBQKoV6vI5VK4cSJE5DL5WQTzijK09PTKBQKqFQq5OzDjDoWFxfJI4D1NhjVmXkbMPIPWxkNcWcMf6U9gN1Z2b6TmW/W63UYDAYIhULqwPv9fvKvl0gk8Pv9dCEIhUIk3On1epRY02q1YLfbyaiC+QVEIhG0223w+Xzix8tkMtTrdYjFYvj9fuzbt496AszxlzkKs9VJKBRCoVAgB95oNEomo0qlEj6fj/wG2WSBLdPHx8dx6NAhtFotrKysQKfTodfr0bEUCgWyK0+n0/B6veA4DltbWxS+US6XEYlESKrLuvWFQgFzc3OQSCT4zne+A7lcjng8TtyCZrNJS38WgjIxMQHglrZCo9EgmUyiUCig2WzSiHO4598bhsW/BxQKBWQyGWi12tvit2KxGCKRCObm5siuu9FowGg0Ev01HA6D4zg6OVlHmnHkt7a2yOyz0WigWCxiZGQEvV4PDocDGxsb5Jm3srKCWCwGPp+PYrFI4zqdTge1Wo2zZ88iGAyS7121WkUul8P09DQmJiawtbVFrMBisYh6vU6yWx6Ph4mJCfT7ffR6PbhcLrTbbSQSCTImdTgcEAqFlDHAPPmkUilcLheRbRjnIJ1OY2pqCidOnIBer8fCwgJisRhGRkaIDsziufbt24dYLAapVAqVSoWpqSkS76yurkIkEuGnfuqnSOHInI8YazGZTNIKjXktDPHaGBb/HtBoNGCxWIh3zvbXzJuPNdsGgwFqtRoRcDKZDDXLmHee3+8nfXypVCLWIOt06/V6cv5pNBqUEsSsvZrNJmQyGUlphUIhQqEQZfdZLBYqDIPBgHw+T0W7sbGBYrEIs9mMiYkJRKNRuggolUr0+30yICkWi+A4DuFwmMaLvV4PpVKJViIikQgmkwnJZBJyuRyjo6O4cuUKdnZ2UK/XIRQKkc1m8eKLLxIbr9Pp0AVQJpNhfX0dGo2GbMZZviGjM1cqFYRCIfT7fTzwwAM0vWC0YbFYDIPBgE6nA+CW6xKTUQ/x2uBxQ/HzHfHrv/7rMBqNiMVi0Gg0MJvNmJ+fp5OfNeQYl5+N0iqVCjHf2Nyc7Z+tVivy+Typ2qRSKbxeL0lUGYVVr9ej2+3ScrjX60EikdDFhrn1Hj58GKVSCT/4wQ+IQOR0OmkWbrFYsL6+TuYaLAyEKeFY51yv12NycpIMNZkij1GGy+UyHA4HddRfPlrsdDpQKpVot9v030KhEFKplHoKLGaLZQ/kcjlwHIfDhw+TXmAwGCAYDEIoFCIej0MkEuHFF1/EyZMnMTk5SaIeZgba6XRQKpUgk8lIMnzu3Lm7fNb828fwzr8HGAwGGAwG2usKhUKMj4/T8lgikSCbzaJQKNymPx8MBuSdzzT+er0erVaLxmydTgepVAqjo6NkTpFMJrF//34At9h8a2trCIVCpCuYm5vD8ePHsbOzc5utWK/Xg0qlgs1mg8FggFwux/Xr12E0GrGxsUEUX9Y4m5qaQr1ex8jICOx2OzY2NpBIJCj6SqvVolQqkccAMxIRCoXk3c+IPcxNiKUGszQiRoJiKwGtVkusQ+YB2Ol0MDk5ScpB5lykVqsRi8VoJfPkk08iGAyi1WphenqaItKYIYhQKCQ77yHujGHx7wEscMNkMiEej5M8ttlsQqFQ4MSJE7BarVheXoZAIKAleqPRoCU6n89Ho9GAXq+nqClGd2WhH8lkEsVikSy+2B791KlTcDgcuHr1KnkAbm9vY3t7m1YNW1tbUCqVUKlU8Pl8sFqtqNfrxBhMJpOoVCok91Wr1ahWq2SuWSwWicXH+hXtdhtms5mUiBsbG1AoFIjH47RlYTbZzMOg2+3S/J/FZvN4PMhkMqLrttttVKtViMViiEQiuN1umEwmVKtVRCIRFAoFeDwebG5uEmOSXQwZgWpjY4MYgMxvoFarIR6Po9Fo3LVz5V7CsPj3AIPBgHa7jUqlAo7jyJee+fBbrVZcuHABa2trUCqVSKfTZNwpEAhgMBjQ7XYp1rtYLKLb7VLzjakD2Z00FAohlUqRy+709DS0Wi1Ji9fW1mAwGCCTyehxFhPG4rt4PB4FaDz00EN47rnnsLKygk6nA5VKhX6/j0qlArPZjEajgWg0CpVKBavVStoFNlfvdDp0MWAXEXZnf/lo7eVyWzZ3Z+YmYrGYuPnMAp2tZJgPXyQSITETx3E4ePAgcrkcWZWpVCqkUilwHAebzYZwOAyhUEgBIslkklYtQ9wZw+LfAy5fvkx5dNvb2/D7/SSEmZ6exs7ODhKJBCYnJ9FqtWjfWq/X6cRmzbxUKgUAJMeVyWQIBoPY2toimS0zxThx4gRCoRDtzdVqNXQ6HWkBRCIR7cVbrRaFcw4GA/B4PKhUKsTjcfzgBz9AtVrF7Ows+ePX63VSy7EkYbYtYRmA0WgU169fpwQhNp5joiVGpWW2WkzUI5fLIZFIaAvQ7/fJcIO9D/MCYIKcTCZDY1S1Wg2FQgGbzQa5XI4rV67QioRdPJgteC6Xw9///d9jamoKo6OjeNOb3jSc8+8Rw19pD7h58yYOHjyIZrNJWv5er4f7778f6XQawWAQ1WqV9PbMcsvv95PWn90lAVCjkBUp69CLxWLodDqUy2Vi91mtVhw/fhz5fB6hUAhjY2PEGmSNvZeP/dhKotVqUf/gxRdfhNfrxezsLDHkmMHH9vY2WWW1Wi1kMhnaJoTDYVrGs7huAJBIJJSQk8lk6I7PosmY2u/ld31mbjIYDMiinNGQ6/U6uQXzeDx0u12USiW6eLGcQo7j4PP54HQ6kc1mUSqVKBfx2WefxeXLl/Hoo4+Cx+PRNmGIH41h8e8Bk5OTsNls0Gq1qFarKJVK6Pf78Hq9uHjxIhly5nI54vAz0Y5er4fZbCYBD5/PR7fbJX2AzWbD2NgYmWsyf7/BYIBAIIBwOExqOubjL5PJYDQaUSqVAOA20o1EIkGpVIJEIoFCocDExAQ59mQyGRoHsn14Pp9HIpGgvTkj2PD5fArkGAwGEIvFMJvNpMMXiUSkYWC6feZezPgMjLrLGoIsv4BtoZj7L1spMR++YDCIXq+Hra0tVKtVnD59GgaDgfohrLFqs9nQbDZhNBqRy+WwubkJp9MJh8Nxd0+YewTD4t8DKpUKVlZW8BM/8RO0pFUqlYjFYmRsqdfrMTo6CovFgkAggG63C4FAQL5/LLWW3UVTqRQUCgUKhQIt0Xk8Hmw2GxVNs9lEIpEAcMukk+n3WYe/0+lAJpMBuNVE02q1FIzJeO6XL18Gj8fD1NQUAECr1UIqlSIYDCKZTJKDcDgcJrUgM+Cs1+uo1+twu90wGo10wdDr9RCJRJTMw8Z87E7OlvjMRpw5+tRqNXIHZrLbcrkMq9UKg8GAer2OnZ0dSKVS2Gw2svMWi8Xwer2Ynp5Gv99Hu93G1tbWbTLjsbExHDp0CFNTUyRLHuK1MSz+PcBsNiMYDOLq1avkgtPpdBAIBMhymjnPBAIB6qqz5Ssz1WCadJfLhWq1in6/j3Q6TcYVL98363Q6mEwmBINBLC0t4fDhw6Ruc7lcZCPGrLqYdv/lcuFisQiHw4FqtUr++az4hEIhisUiSYJfPvrj8/kQi8UYGxujrALWtGSBoQKBAGKxGDwej6y/mOCIEaDYPB4AafFZ7Dh7HjMw1Wq1OHLkCEVyVyoV+P1+ACBFn0AgQLlcppThWq2GQqGAzc1NklOzfsQQd8aw+PcAtvfOZDJEPWXjvHK5DK1WC5/Ph16vhytXrsBsNlMBKhQKrKysIBgMwmAwQCAQwOl0wm63o91uI5fLkQJQKpWi2+1Cr9ej1+uhXC5jZGSECpvNsZmT7draGmKxGHq9HllXMTtsVvBstcECQwCQ+QXrEzAHH7FYDJPJRLFg7E+r1SIBDZPUajQamhgoFApYrVasra3BarWi3W7Tvp+JiJrNJukh2L5eIpHAaDSi0+kgk8mQsKfdbqNcLmMwGMDhcMBkMtFoNB6PkwdhpVKBWq1Gr9dDu93G4uIiAoEADAbDMKxzDxgW/x7g8/mwtLREdlg6nQ4bGxuo1WrEnhOLxTQrN5vN1O1ut9sYHx+ndFyLxYLNzU0AIGYcW7onEgm64wO37MMsFgvMZjMVBWPcMYtsFqLRbDap+cZUc2xvzpqCLAUHuGWN1el0SKzEEns2NzcRjUah1+thsVhgMplQKBTIHw8ANSqZiIaNA5ndF3M4rlarJElmS/iXf1a9Xsd9991Hnoe1Wg0GgwGFQoH6Jq1WC9lsFr1eD2tra0gkEjAYDNQP0Ov1iMVisFgsRHPOZrN4+OGH78KZcm9hWPx7AOPQ22w28raLx+OIx+NQqVQAQDNodsfm8/lIpVLY2toiVl+pVCL6K+PMx+NxWK1WMrlkJqBTU1MwmUxoNpsIBAIkxmFsQeaww9iF7LVsC1CpVLC5uQmbzUahlix0hDXhmCBIJBKRCw8rvl6vR2M9dhdny3HWuW+32yTrlcvltOwvFAqIx+PI5XLodDq3rSpY0bNVgVarhclkQqPRoLEji91yuVxk0c2Sh9kFkHX7DQYD7fMZocpsNt/lM+bewLD494DV1VW6EyqVSly5cgVGo5H2zDs7OxTE+fJwiUKhAD6fj4WFBZpZs9UCW45LpVLiozNpLI/Ho6X48vIyUXJZd5+FgEilUuTzeXLXZboBxuDrdrukMmS+gyxu/OV21ywJiPnjsWZhMpkkiywmYGKBJExR53A4EAgEwHEcdDod+Hw+5ufnKdyEfQ+pVIp6vQ6Xy0VMwLm5OUo2Zs7B+XwecrkcGo2GyEhMr282m4k5OTMzg2AwCLPZjLm5OWSzWVSrVYRCIczOzt7dE+YewbD49wBGv52bm4NKpcLGxgZRd/1+P9rtNlZXVyGXy/HmN78ZFouF9qYqlQrlchmnT5+GWCwmX/qZmRnIZDKitTIHX7/fT4055l4jFotpGc2IMowCq1arKW5LLpcjFoshn8/TCI7Fcfd6PXg8HuL1OxwOlMtlmM1mst9mWXr5fJ4SiAGQyKjT6cBgMEAkEkGj0VDGQLfbxdLSEl0cmNU4cKub3+v1KAVod3cXNpsNDz30ECwWCy5cuICtrS089NBDNPVg0wCdTofNzU1SHNbrdRw/fpyswWdmZhAOh2mVcP/999NKZIg7Y1j8e4DdbifhSLfbhVarxaVLl6DX6zEzM4NyuYxYLIZYLIabN2/i6NGj6HQ6ZKbB6LvArfQfh8OBRCKBSqVCrrlSqRQjIyMUORWLxcDj8WisBvz/RqKdTofcahmTMBAIQKFQIJvNwmq1IhaLwWw2Q6PRIBKJEPXV7XaTAYlYLIbD4YDFYqFVSyKRQCKRwMrKCmQyGTKZDDUumYsQ8/ZjpiISiYScdZj5aL1eBwAqYrvdjmPHjkEoFJL5RqlUQiaTIRckn8+H/fv34wc/+AF2d3exvr6OdDoNHo8Ht9tNXIJms4l0Ok0WZtlslvoMR44cgdPpvGvnyr2EYfHvAfV6nZp6m5ubSCaT5NJrsViQSqUgFAoxOTmJ7e1tZLNZ7Nu3D+VyGclkEgsLC7fFTdvtdvD5fGrKyeVy2r+n02kanQG3cgG0Wi3W19fJwpqNvhi5yGw2kz0YMwadmJiA1WpFt9tFLBaD1WpFtVqlJJ12u43JyUm607M7JtuDs8RfsVhMqwCn00mUZaVSCYlEgkwmg0gkgng8Dp/Ph8FgAKfTiXK5jGg0Sj57lUqFnHtYgrBOp8PIyAjkcjn0ej1NPdhMn2kgcrkcdnZ2KI+Q9QHa7TZUKhU1GxknQavV3t0T5h7BsPj3gAceeIBouswtlhV3MBhEOp0mxd7Ro0extbVFWwOj0Uj7/UKhgFgsRncvhUKBYrFIDUWRSESdcDamk0qldPczmUwQi8WUp8cCRPr9PkZHR1EoFGAymaDX6+H1erGxsYHLly+TxJb1LBgLj/kRMhFQqVRCr9cjmzEmEGLpP8xROBaLoVAo0Agwm82Skm9mZgY6nQ5PPfUUXSjsdjupDNfX1wHcUkoyr/5kMgmHw4FQKEQrC2Y8qtVqwefzKSZdKBRibm6Ogk53dnagVqspOIVFm4+MjNzls+bfPobFvwc4nU4EAgHqJGs0GqKY7uzsYGlp6bZ9+/3330/Gm4VCAfv374dWq0UqlcLGxgYCgQBRZlkHnC2NA4EA5fjxeDy6izGuvFQqpf08S71hOXaMiafX67G6uopoNEozdtaEZHtxh8OBVqtFRiHhcJhyCRgFmU042MVLKBQSrZhNK5gK0el00gVGIpGg3W7D5XKh0+ng5MmTkEgkFAj61FNPYXFxERMTEzSFWF5exvj4OG7cuAGfzweVSoV8Pg+VSoVoNErJvtPT0+j1euTyy7gKWq2WPBGFQuGw+PeAYfHvASyYglFuOY4jWqnL5YLb7UY8Hif7LoVCgUAgAJPJhGg0ing8DrfbjXq9Dq1Wi0ajAYFAAKPRCLVaTR55bEkOgGbjtVrtNvlvq9WCQqG4zS2YjQSZJ18qlaKkHKVSSYQkdrFgvYVsNkteAO12G0ajEfV6nTwCut0uAoEA1Go1Dh48iHQ6DalUCr1ej0wmA4FAQOM8Rtphe3Cr1QqFQoHx8XEKA9VoNJifn6dlOos/q1arND5Vq9UAgEgkQisjNrJklmhshcR0COVymS5k1WqVfAOGeG0Mi38PKJVKKBQK4DgOnU4H/X4f6+vrOH78OAlJ/H4/gsEgstksNjc34ff74fV6Ua/X8dxzz5EohRUtC77QaDTg8/lYX18nR16JRIJgMEhqOzbaY7z9ZDKJeDwOm82GyclJHDhwAP1+H4uLi9jd3UUmk0E6nQafzydPgVAoBLFYjLm5OYhEIuzs7EClUtE2IJfLQSgUwu12I51OYzAYwGw2QyaTkU8ga/Kl02mkUikYjUZIpVJMTk6S7x5r5o2MjKBardI0QqvVIpfLkfSZxXbp9XqMjY1BqVRS+MbS0hIajQaOHTsGo9FI5iTNZpNcj8PhMGUNFgoFHDhwAOVyGfl8nkhTQ7w2hsW/B4RCIWp4sZk5a4Ix4wkWmc268czUQiaTYXR0lPzzWXx0oVAgMhAjxyiVSoyMjJDajTXx3G43ms0mBWsAINVcLpdDKpVCPp/H+fPnyYuPUXNVKhX0ej2l5ayvr1PHn6002GQik8kAuGXKwfbqU1NTeOGFF5DP52EymRCLxTA/P4/NzU0YjUbq4sfjcXIjZlTlWq0GjuNgNpuxsLCAWq0Go9GIRqNBOn+2CmAN1Gq1igsXLkCn01HcGDNNLZfLqFQqWFhYwOTkJBwOB7LZLKLRKG7cuEE8ASZiGuK1MSz+PYBp49vtNhldMGMPp9NJjrvMvFMmkyEajWJjYwOFQgHHjx/H+Pg4Go0GcrkcNjY2EIvF0O/3yaSCEXCYGSUj4jAPAB6PB4lEApfLhVarhVAohHa7jYWFBayurqLT6dymEGTkGh6PB4PBgHg8ThcMxvtfW1uj8R27YDB2n1arRTabRafToT22TCbDzs4O0uk0FAoFTp48SUzGWq1GDTqdTodutwuj0UjOOyaTiSYOjKhjs9lo3Mnov263Gw888ADOnz+Pz3/+87BarZicnKTtQa/Xw8GDBymd6Pnnn8fMzAz27duHSqVCjcsh7oxh8e8BLAiTudza7XbY7Xbs7OwgHo8jkUiQqu3lI6erV6/i8uXLlDfHlsXBYJD2x6xo2LiPzfeZbJhdMBj3nTUNmS6eUYatVissFgsx+FjPgE0NPB4PkskkqQm3t7cp225kZASXLl0i9SHT44+NjSEQCKDRaJCJJuPTM28/nU5HjUXmWsRSgOPxOJl2OBwOIgKxeDIejwefzweRSIRMJkNzfNZ0jEQisNlslGLUarWQTqexs7ODqakpKBQKnD17FgKBADKZjLwAL1++fLdPmXsCw+LfA1jopU6no1mzUqmEzWaD1+ul5TsrmtXVVbjdbthsNuzbtw+rq6u4ceMGWVUzVdrGxgbt61mGHiPusJk38+nv9XqIx+PEymNL516vB4PBQGnBjI+QSqWoUy4QCHD27Fmo1Wqsra3RKqXRaCAQCKBQKGB7exvlcpkssMbHxyGXy7G9vQ2pVAqr1QqBQEB9Co1GQysD5jnAmpYs0pvpHfL5PLxeL0wmExmH2Gw2OBwOGAwGhEIhMhlhPn7MGzEQCGB1dRX5fB5utxtzc3MYDAY4c+YM6RlqtRoxGavVKtLp9N08Xe4ZDIt/D7Db7dDr9fB4PLRHVqvVmJ6eRj6fJ/fbUqlE5hSM4NPpdDA3N4dMJkPqtHa7jc3Nzdt49RKJhJbdjEEH3Ar9ZDZWIpEIIpEI1WoVEokEJpMJxWKRpMVMO6DVatFsNqHValGv13Hq1CnY7XbaTrRaLdhsNmresVVNtVq97YL23e9+lzrpLpeLLLgEAgGNOk0mEyXypFIp2sOz7QybUmxsbFBMOYvxXl1dxcjICKamppBMJiGVSpHNZilYRCKRoF6v4/Lly7fxDh544AFIpVISFrELkMFgoBXCEHfGsPj3AEbpLZfLlD1nsVhgtVqxu7sLq9WKWq0Gp9MJgUCAWq0Gv9+P++67j3zmVlZW6E62s7MDsVgMvV4PAOTiU6lUaJSYTqehUqnAcRzy+TzNzFkTjPUEDAYDyuUyddSLxSJZZDWbTej1egwGA+RyOZRKJXLbXV1dpcAMi8VCzr2sQVgqlUhLoFQqIZPJsLGxQRcg9lin00E6nSZ/gG63i5s3b0IqlcJiseDAgQOUCsy+KyMmAaDsQhb13Wq1IJfLsbCwgPHxcXoN2660Wi1sb29jbW0N09PTNEqMRCIYGxtDLpcjTcIQr41h8e8ByWQSW1tbGBkZoSW2w+GAVCrF/fffj8XFReTzeXg8Huh0OuTzecrrc7lcJJgZGRkBn8+Hy+VCOp1GNBqFRqOBTCbD1tYWgFuuQcxthxl49Ho9suQuFovodDrkpmO321Eul0kqy9RzzERDr9djd3cX/X4fGxsbEAgEaDQaNApkzUpmLS6VSrG0tIRmswm73U5uwczh1+FwwGg0kpKQkXdYACjLJOj3+6TfZ7yGdDpNlONisUjxX/1+HyMjIxRh5nK5cOjQIdomsTgx5jrM+idra2tkfrK0tET8BmZaMsRrY1j8e4BarSapLgDK7WMU03a7DbfbDaVSCYVCgZmZGbRaLUSjUXi9XrTbbbL+drvdEAqFcDqdtExne9TBYED6eZYAxBh829vb5JHHlt4qlYpIOax42Vzf7XaTrt/v98NsNmN3d5eENcw1mIl6LBYLarUa9u/fj8FggIWFBQgEAphMJrjdbmoOsu4/C8xkj5vNZiwuLhKfwGKxUJQ5S9Th8Xg0MmVNwYsXL6JUKuFnfuZnoNPpcODAAZhMJrhcLsTjcdoqVKtVanjmcjliB7LUopmZGSSTSaysrGBycvJuni73DIbFvwewkVm73Ua9XidVnlQqpZEbG9Hx+XwqksFgQCcw48UzcQ1LAGJ79VOnTlEk1+HDh1Gr1eiuaDAYsL6+Tu/PTETZ/pqNAxkBSSAQkNmFx+OBRqOhlCG32w2xWIyRkRG0221aHTA7bzaCO3z4MFqtFhGbWJOSNSzL5TIMBgM0Gg3pEVjT02g0QqPRkDOPQCCAXC6nC1Qul4Pb7SbLcdZfYLN9VsyxWAzFYpGYjd1uF4cOHUI4HAafz8fY2Bj1A8bHxzE1NYV+v098hSFeG8Ogzj3g+9//PsLhMMLhMOx2O52Qo6OjFFtlMpmgVCrprspGXIPBADabje6SLPY6HA4jHo8jEAjA7/fTXrff72NiYoLsvZiDbjKZRKlUojtdo9FAp9OBQCAAx3Fot9uk9QdAqjmpVAqfz4dwOEzbCEYbZn4C7CIzOjoKh8OB8+fPw+FwUPhntVpFJpPB2NgYwuEw+RswqNVqEgexOb7FYqFo8UAgQKIc5h6czWahUCigUqmg1WppNMiIUOxCI5VKMTc3R+adrJHY7XZx+PBhmo4wiXOlUoHVasWjjz56t06XewbDO/8ewOyzrFYrmVdUq1UAwMTEBMlhGe/8mWeeQa/Xw9jYGJl5MuspRn/tdruwWq1kz8X89tg8nxVqOp0Gx3FQqVSYm5uDXC7H5cuX0e12oVAo0Gq1iBPA5/PhdrtpNMh8AFgfYDAYIJ1Oo16vo9VqwW630xRCr9ejXq8jlUqR6zBblTDSTy6Xo+8gEonQaDToTs1iuwGQEo9RbQ0GA5RKJYrFIqUK2Ww2GhUyHoBOp8Py8jKkUikymQx9N6PRiNnZWZIMK5VKUlnyeDxoNBrodDrSXPB4vLt2rtxL4N/tA7gX0Gg0qNvMls48Hg/JZBIASAHHlvfT09O0DTAajUS2YQYaLL6Kzfg1Gg3N3VnhdDod7OzsoN/vU4d/d3cXIpGIZuIulwtqtZpm5qOjo9QTYLl9crkcyWSShDPMPmxsbIyixO12O1ltJZNJMh69du0afTafz4dGo8HJkycxMTEBACT9rdfrZN5ZKpUQCoXogsZSelmcdj6fx/j4OPU2mNHJ1tYWUqkU9Ho9TCYTOSI3m03qFTAdP7Mak0qlEAqFtPVRKpVkhjLEnTG88+8BMpmMVGVCoRDtdhtTU1NoNBqYn58Hn8/Hm9/8ZggEAoTDYRiNRpw7d472woy0AoBYdKx5uLi4CL/fj9XVVSSTSXpePp9HMpmE1+uli4RMJkMsFoPf7yelHHDrzs5WDCKRCLVaDT6fj+b1LLW20+mQkWi73YZGoyG2HmPbbW5uwmQy0Zw9mUxSMClrMjocDrIVY9wFt9tNKwWj0QitVotMJgOFQgGJRIJarYbd3V0aZ7LVE0sGymQy4PF4NDZlVl5MfMS0Er1ej0aZHMdRTiDjL7DffIg7Y1j8ewCzg2aNvU6ng7GxMfR6PXS7XVgsFuLhdzodBINBSr2RyWRoNBowGAwUb8U8/9hsPxaLUeAGC69khQ3gtmV6tVqF1WolZR8L32BBFUajESqVCjMzM3A4HFhYWCAtAusRMPpws9mk7Ua9Xifm4sv9+rLZLHXc2YWrWCyS+IjjOESjURiNRhIyud1u5HI5VCoVBAIBmM1m5PN5RKNRChwNhULUD2H9jk6ng/HxcSQSCZRKJej1ehQKBXQ6HQCgCDIWHCqRSGglwLIAWMNziDtjWPx7wM2bN7G9vY3p6WkYDAZS0ykUCtx///2o1WqkPOv3+8jlcrcx3thdOhgMot1uU4iGUCjE6OgoqtUqzGYzSqUSXQyy2Sy8Xi+l2bKegdlsptXFO9/5TkrcWVpaQjQaRTKZxMjICHXlm80mMfRyuRyKxSJMJhPRbZmZB7PiZl77RqMR+XweOp0OhUIBJ06cgFgsRqvVoiDRsbExyOVyjIyMkDJRKpWSU/Dk5CTFcev1eqjVaoTDYXzhC1+ARqPBkSNHiMrM6NDb29uQSCR4+9vfjuXlZZLtMm+CwWBAUmXg/1cgsufJZLLhnX+PGBb/HhAIBKgAmYU2E5Owk5DJTSUSCaampsg7npFsOI6jlUC9XifTy3a7TdHYwK1VhtlsJq38xsYGMpkMuemwFF6/309mIGwZPBgMSH/fbDaxurpKzTy32w2Hw4F0Ok2uPGzEl0wmaR/e7XZRqVSg0+moacjoyA6HA2azGQ6Hgzj+jUaDlIPHjh1DJpNBPB6H1+uliQSbdBw6dIhsu8bHx8k8RC6XY3p6mlY/GxsbCIVCEAqF6Pf7ZHbC+hLtdhtCoZBWL61WixyOWCNyiDtjWPx7wObmJiwWCzWfut0ukskkUXsnJyfJ1kqtVqNcLlMQRjKZRLfbJWssFvPtcrmoCZjP52/LyOt0OhTkIZfLSSWYzWaJdBMKhRAIBHD69Gk0Gg1oNBocO3YMqVSKEoPr9TomJiZodSAWi2Gz2eiYEokEzdrZ6oNRbfl8Ps3tmVswMyFl7MW1tTXI5XJ4PB643W6oVCpK2y0UCq8QJg0GA4yOjsLn82F0dBR6vZ7MO3k8HqrVKjQaDXEkKpUKWZQxlSLTF3Q6HboIcBxH/gd8Ph/pdBpHjhy526fNv3kMi38PkMvl1PG/du0aDh48SOYVZrMZ1WqVxDSsmVar1RAMBqnjLhAIyOra4XAQAYjH46HX66HRaOD48eNUONFoFL1ej5578+ZN8Pl8ou5GIhGo1WoEAgHU63Vi2cXjcSpWxvYzGo1QKpWo1+vY3NyEWq3Gvn37KOW3Wq3S7FylUqHVaqFarZJJaT6fh1KpJOsuJjLiOI5m/Ds7O0gkEtBoNABA6TssgJRtF/L5PGw2GzUF5XI5DAYDCZ6MRiOsViscDgdUKhVisRg2Nzfx1a9+FTMzMxgZGUGhUCD+Prvbl0olrK2tIZlMgsfj4V3vetfdPGXuCQyLfw9g4ZMsYScQCBDbju2P2YyeKfL6/T5WV1chk8kwOTlJy1s2Gksmk5DL5QgEApiZmcHm5iYWFxfh8/loK5HNZuF2uyGVStFsNsn0QqfTkSbAZDIhmUwiHA5Dr9dThJjP56PtABvlmUwmugi1Wi0Ui0UkEgmIRCIcOnSI7uzPPPMMjcuYcaZGo0Emk0EymSRhDksIYrkBbPzp8XhgNBohl8spZYct/9l32d3dhdPppP37yyO8z507R+pJsViM1dVV7O7ukk9BJpPB1NQUaSacTidWVlaI3z/07d8bhsW/B1gsFjoRzWYzFblWq0WlUsHy8jLOnTsHkUgEgUBATjssyZbtfbvdLsrlMra2tjAYDHDw4EEUi0V885vfBABoNBoi3jDPOiYVttvtaDabUKvVZITRaDTg9Xqh0Whw/vx5yGQyEvuw/L7FxUUcPXoUDocDQqEQlUqFKMmMWqvRaGhrAty66IyNjRHhRqVSQaFQoFKpYGtri+SzbJSYSCRQq9XgdrvJkptdMAUCASKRCJaXl2G322krsLq6ilqtBrvdjqtXr6LValEuQalUIovyfr8PsVgMtVqNQqGAZDIJjuMwPz8PnU4Ho9GIWq2GbDaLZDKJfr8Pm812N0+XewbD4t8DGMuMdfNLpRLUajVJWFUqFYlu2N02n8/DYDAAABlkMDIKC6NsNpuIRCJwOp3kd18oFGi/brfb4fV6cfPmTXLuEQgEEAqFtyXVulwuHD9+HBzHUfZdvV5HIpGgLnk2m0U+nycTzV6vB4VCQfFarLPPxpGMtMP8CWKxGMbGxpDJZKDT6ciBh3kRjoyMQKFQUM7g1tYWTCYTrl27Rg5CkUiEWHqs4QmApgECgQDJZBKRSITSjwuFAoxGIwwGAxYWFpBIJOBwOGgisrW1BY1GA7PZjDNnzmB5eRnxePyunSv3EobFvwecPXsWy8vLEAqF5BNvNBrJ0KJYLOKpp56CWq2mbn40GoVWq6XHbDYbRCIRLly4ALPZjJmZGWLSCQQCaoJdvXoVfD6fVGxsWd5qtShJl9ln22w26HQ6srIWi8U4evQoLl26RL4DzJp7YmICPB6PLLei0ShcLheAW8UXCASgVCpJzssCR0KhEBlpRKNRzM7Owmq1AgClEpvNZhw8eBB2ux2dTge5XA43b95Er9ej5x47dgxerxc8Hg+bm5sk92Vuxevr69Dr9RCLxUgmk5iZmYHZbEYgEECz2YTBYIDZbMbq6ip2dnZgsVggEomIf1AoFKhnwAhUQ7w2hsW/B7BGHds/9/t9ZLNZxGIx8vMXiUSYmZmBUCjE888/T7JWm82Gl156CS6Xi+KlDhw4ALPZjBs3bsBgMODw4cOkA2CRWKlUivbnzHFHq9WS0aVSqUSz2cTNmzdhsViIaciYgqzHIJfL4fV6cfbsWfD5fGxsbAAAlEolxGIxSqUSddS/+c1vkuMui+NixqVMfMOSeGUyGZrNJsLhMKamphAOh/HSSy/B6/VCJpMRQ9Dj8eCFF16AWq3G6OgoHdMzzzxDjkAsLgwAOSYxUxOxWEw8hmw2S6sFh8NB0ml2AazX64jH4zh16tRdO1fuJQyLfw946qmnIBQKcfjwYSpGPp8Pk8lEHvtvetObYLfbKbxCq9Vi3759uHHjBlKpFPbt20dUV4vFQnvtc+fO0RZgc3MT6+vrOHDgAKxWK/7hH/4BOzs7OHDgAHQ6HTqdDplmdjodTE9Po16v4/r165Tlx0ZlfD6fmIV2u53y9e6//360Wi2IRCLIZDKUy2VMTU2hWq3eRpc1mUyo1+uQyWREG2ako3w+j0AggE6nQ/FgrK8RiUQwPT0Nl8sFvV6PaDQKAPjOd76DZDIJnU6HcDiMwWAArVZLtF/GNmR2YUqlkqLPS6USuQuztONWq0WpRnw+n3ITu90uVlZW7vIZc29gWPx7hMlkgsfjIVOPb33rW9BoNKjX63QH3t3dxcTEBPR6PVwuF/h8Pu1xm80mDh8+DI1Gg1arRYSZYrGIhYUFrK2tUSKuWq2GWCzG5OQkgsEgYrEYSqUShVJGo1Gy3YpGozAYDLjvvvswPz9PYiBmsqFUKjExMYF2u435+Xmi4jJqMACIxWLw+XwcP34cPB6PdPSDwQDVahWlUonGiclkEr1eD2KxGOl0mkaFEomEXIFKpRK2t7fJ8ttms+HMmTNEAWajSIFAgFQqBYlEQm5ETEqcSqVQLBZhtVrB4/Gwvr5O5qhKpZKW/WyFwvz+VldXceXKFTz++ON3+Yz5t49h8e8BVqsVVquVuvmsK88604cOHUKlUoHX60UwGMTW1haOHj2KRqOBiYkJzM3NkdsvAOzu7gIA6QQAIBqNolgs4tSpU3jppZcgEomgVqsxMjKC7e1tqNVqsr4qFotkVqnVamnywGK4mGW2WCxGpVLBhQsXUCwWcfPmTSLhsP0xe45Wq6WYrgMHDmB+fh65XA4+n4+UetVqFe12GxaLBcCtBOF4PI6pqSnSAPh8Pmi1WiI4MdMR4NaSnl1YSqUSkY0sFgsxDn0+H1qtFmKxGMWLWa1W3Hfffbh58yZEIhH27duHXC5Hq6d6vU4Cpmq1SlLmIV4bw+LfA9gsuVwu013SYDBgbGyMTuhut4urV6+iUqnAYrEgEAjAbrdTVDYAMq0cDAZErY1EIrh69SrZbl+6dAmtVgtOp5Psq1lMF1v6CoVCxONxGAwG2Gw2SCQSLC8vE0OPGWRsbW2hXC7jhRdeoD1+q9UiGjCT2AKgyLDDhw/j8OHDiEajqFarNFVgzsISiQTRaBRSqZTCOViK8ObmJqanp6k3wIhH9XodwWAQmUyGdPgAqJF3/Phx7Nu3j5yBvvWtb5FZ6ejoKOx2OzQaDebm5lCv18kfIBAI3ObWm0wmMTk5OWT37RHD4t8DotEoHA4HGWywZfnm5iampqbA5/ORyWRw//33Y3JyEu12G08//TQikQhqtRrW19ehVqthtVrJ9oqlAHEch3K5DKPRSGw+uVwOlUpFs3i3241AIAC5XE5x3mazmdyDWQioQqGAw+HAyMgIGo0GwuEwFAoFcQOYG7BIJILBYEAmk8FgMEAsFkMmk4HBYCAb8PHxcdjtdty8eRM2mw0mk4nivLvdLrLZLAQCAcbGxmCz2cDn82m5bzabMTIyApfLRUrAjY0NBINB3HfffXjooYdw8+ZNPPPMM7RqMZvNtA1gDUu73Q6j0QiTyURRZSwiLRAIIJfL0XZAJBJhMBhQY/Bnf/Zn7/Zp828ew+LfA1iB8Pl87OzswOv1km9+LpdDLBZDs9nE6dOnoVKpbnPUWV9fJ6NOlkajUChIijoyMoJTp04R408ikUCtVqNarUIsFqNcLtMdutFowG63Q61Wo9/vI51OY2VlhQhFLMCjWq3S1oKJZxjHoNFoQKfTYWJiArFYDEajkVKFG40GIpEIKpUKzp07R98LuEXXnZ2dRSQSQaFQoFxCJuMtFosky5XL5UQFZrJftj2p1WrQ6XQYGxvD7u4uvF4vZmdnYbfbkUqlqPnHjD6Y7x9zH2bkIpPJBJvNRkzAYDBIen5msjLEa2NY/HvAoUOHoNPpaK/KrKSZeIdlzDFJ7qVLl2jZy3EcFhcXydyDOfKWy2W0223qZgPA6uoqMepYfl6tVqP5N6MNq9Vq5HI5NJtNiMVi8hTc3t5Gp9OhC8b999+P8+fPk2kGs7U2Go00rjt58iRGRkZw7do1PPPMM6TqW1paIjqtTCYDj8fD9vY2Jeay2X+73SbLb5a9x8aDwK2MwWazSbFkjCzFPAKYIYrX6yV7bp1OR/ZlW1tbcDqd6Pf7qFQq2NjYIGLTsWPHKFVoMBggm81SAMoQd8aw+PcAo9FIe+TBYIBCoQCz2Qy5XI7JyUns7OxQbPT4+DhqtRpFaDOHnUQiAY/Hg8XFRayursJut1OwZLfbRSqVIlotY7R1Oh0yBWHhGaFQCOl0Gmq1mvb4169fR7fbhcvlws7ODtlmsQsJ0+2zbQSz506n03jxxRcRCoWwvLxMHH61Wo2VlRUUCgVIpVIoFArodDqIxWLMzMxALpfjypUrmJmZISlyKBRCpVKBSCTCgQMHiCDE1H06nQ5+vx/j4+PEYmSrFxbrxaTOfr8fCoUC8Xgc4XAYKysrOH36NOx2OxwOB00xQqEQXC4XnE4nNBoNpFIpotEojh07dpfPmHsDw+LfAxwOB3g8HoRCIRKJBBFLJBIJisUiSWU9Hg+p5zqdDpaXl5FIJGCxWGh5KpFIcPz4cTLDZN59zWYTRqORJKvZbBY8Ho+aaqzzbTKZyPkmkUgglUrBZrNBq9VSbBjjyTPjUNY8GxkZIW29QCAgx5v19XX6bLavNpvNdPdmmYAejwfLy8sAbk0qUqkUjEYj+RpqtVqMj49TE9JgMNDqhqUEs2OPxWIQCAQYHx/HyZMnkcvlYDQaaQXgdrvxta99DSaTCXa7HWtra6jX65idnaXv0Gg0sLOzg2azSbmH5XIZhULhrp0r9xKGxb8HKBQKRKNRyppndNsbN25gY2OD9uJyuRzBYJDsvFh3nKn6UqkUzGYzLYMnJyfRbDYRCoXA5/MxMzOD9fV1ACDlHFPzmUwmxONxitBOpVI0slOpVAgEAuDz+cjlcrDb7RgfH6fOOBvDse0A69Dr9Xo6FpvNhiNHjmBzc5N88VmElkQiQTqdJukvm+dnMhmEw2G6COr1ejLgqNVqtPIwGo2YmpqiFF82uTAajRgbG4PVaiWVIFuRSKVS+rO0tAS73Y59+/bRmO/b3/42eDwe+RSw+HTmHTDEnTEs/j0glUqh1WpR04zJalkTj43o1tbWIJVK4XQ6EQqFEIvFMDc3R8GTuVyO8u11Oh30ej2l/8zOziIUCtFdminpWOy2SCRCLpdDq9WC2+2GxWLBysoKSWaZ3ReLA/d4POj1esTlX1lZIW2C1WrFgQMH0O12yQ+fXShkMhmMRiNcLhfMZjM2NzeJkMMEMwqFAvV6nYqYmYHs7OxQmvCRI0co7IR911arhXA4jNXVVdhsNpw8eZIUeW63m/wR4vE4MpkMNBoNGabq9XryBmD5h+xiFAgEYLFYcN999yGVSpGnwBCvjWHx7wF+v5+W4szPPhwOo91uk5ONQqGAXC5HPB7HlStXKMmXKdAY157FYLNOutlsRiKRwOHDhyGVSjEzM4N8Pk8sPR6Ph9HRUSwvL0MsFkMsFkMikeDgwYPI5XLIZDKkOmR3vna7TcIa4Fannt1Fe70eHnzwQbjdbqRSKVy4cAF8Ph8ejwdmsxnpdBoOhwNOp5PuqKzfwMI2G40GTSKKxSKazSay2SwZma6trZGRCI/HoyhtsViMXC5HqyGXy4VoNEopPjKZjFx/b9y4QXbdbNLC7MyYTJqRjXZ2dnD9+nVMTk5SYMoQd8aw+PcAZsvF+PwvvfQS5epptVpotVpKqmFjPLPZjEqlgpWVFeKs79+/H1tbW4hEIpR1t7y8fFssdSwWI40A2zqwMBCv14tYLIa1tTXKvWfLfz6fD61Wi0ajQUo+tkQ/ePAg0W+///3vI5vNwuFw4Nq1a7hx4wZcLhe2t7cRDAbhdrtJwcf2z2w1otfryVabeeqxQE+73Q4AaLVa2N3dpQlAIpGgJCGDwUBbjXA4jFwuh0gkQpyCl/c7stksKpUK7HY7Tp06hWw2C5/PRzFfzGyk2WzC4/HQBY7jOMRisbt5utwzGBb/HrCxsQGXy0XJs4wSy3zkotEoMpkM+eG5XC5YLBao1WpkMhm4XC4olUqsrq4inU7TxGBsbIzspwGQZr9arZLQRiaTodvtYnp6GolEAlqtlogu5XIZarUaAoEAFouFmoLM/EKn00EgEOAf/uEfKAxDoVBQXFiv18PExATUajUikQi8Xi915qPRKPh8PvU3gFv03E6ng2QyCZFIRJ59QqGQ7szs+x47dgy9Xg9SqRTdbpf0/WNjY2RZnsvliFhUKpUojfjKlSvo9Xo4fvw4Zmdn4fV6ycpbo9GQ43GpVIJEIsGJEydoO/Xy7IMhXhvD4t8DBoMBLl26hEKhAJ1OR6m93W4XsVgMiUSC7obT09NoNBq4fv06uc5aLBbweDwsLi4imUxienoaUqkU169fh1KpxOOPP46NjQ0kk0k0Gg0sLy9DJpMRV5/xCZjnHruwpNNp4vjXajWaKrDGm81mQ61Wg9/vp7RfZr1VKpWwb98+tNttCgd56KGHsL29jVgsBqlUiuPHj+P73/8+XeQsFgul4bILAmt8siRf9n6M1cianwBIdjs9PQ0ej4d4PA6xWEzBHNFolLgLDzzwAE6ePAmHw0FegWKxGMAtIVKz2UShUMD4+DiuXbtGv8FgMMCBAwfu2rlyL2FY/HvAd77zHXg8Hhw+fBhisRiFQoFOtHK5DJvNhje96U1YWlqCUCiE3+9HsVhELpdDo9HA2toa+eVJJBKsrq7i7W9/O+x2O0qlEoRCIcVwsWQco9GISqUCt9uN9fV1FAoFHDp0CEKhEL1eD9evX0cymYTJZMLU1BSRbVqtFgKBAMlqmdglFouh1WpRw5BZiptMJkSjUSpqNvMfGxsDcIuk43A4YDAY6E6fyWRQLpfR7XYpDJS5ArGVEVvC9/t9ytLTaDRot9uUeciyCKPRKEQiEUKhECKRCDqdDjweDwDQ78y+9+XLl1Gr1RAIBOBwOJDL5SianOM45HI5Ik0N8doYFv8ewOPxyISSJdHI5XLw+XxasqfTaczOzuLixYsQiUQk3eXxeJRF73K5KKWW2X4xF51KpYJer4eRkRFIJBLaz7M7ZyaTwcTEBLxeLzY3N5HL5TAYDLC+vo6pqSnMzMxAqVQiGAxSTDgz6wgGg5Riy1h7AoEAHo8HYrEYRqMRbrcb0WgUiUSCgkGYu87p06cpoUcgEOCll14CAPIDKJVKyOVyOHz4MGQyGaRSKeRyOdlr5XI5pFIpTE5OEvW40+nAbrcTnXdmZgaBQAAAKL4rGAxSE5HRqDmOo+wEtkoYGxtDrVaj1chwz783DIt/D2C2UEw8YrfbEQ6HUSgUIJPJaL/P2HQrKyuQSCTodrtkjFmtVtFqtbCzs4OjR48im81ibm4OiUSCqL7hcJgKyufzQSaTIZvN0mqi0+lgaWmJKL8sHjwUChH/YGJiAg6HAzKZDPF4HBqNBkePHkUoFIJYLH7F/rzZbOInfuInUK1WIZfLiZLM/AZarRZMJhOAW6q5ra0t6rbLZDLi36tUKly5cgVms5l89Rn7bnJyEhcuXMDS0hKsViuOHTsGnU4HpVIJu92OjY0NrK6uwmAwEKswnU7ju9/9LrRaLfR6PUKhEAwGAyYnJ2G321Gv15HP5+HxeNBqtehCoNVqyadgiNfG8FfaA1g3mvnyq1QqVKtV5HI5spAWCAQIhULkm2c2m7G1tUV7btb9397eRjgchlgshsfjQb1eh1QqhUwmg9frJS//ubk54rjHYjE4nU5Eo1FqCrK7uEQigc1mQyQSQavVwunTp8ljkJl9sKCNTqcDjUaDnZ0dtFotukuXSiWi2VosFvj9fkoUZqakzKmHCY8EAgFKpRJ12L1eL0KhEEWZseNptVpIJBJoNBoUaWaz2Yhc5Ha7YTabsb6+Dq1Wi+PHj+PSpUtQq9WUksTo1DKZjBh9vV4PSqWSPBCZYalSqSTK8RCvjWHx7xErKyuIx+MYGxtDqVRCJBKBx+OBSqWimbtWqyU323w+T8o3kUgEs9mMWq1G7rvMsIL569tsNuTzeYRCIfT7fdK+sxCQVCqFRqOBcrlMAhnWAXc6nbh67RoEAgGeffZZTE9PQ6FQYGdnB91uFxzHwWazUSLO5OTkbTHaCwsLt97j6lX0+33s7u7i5MmTOHDgAKrVKng8HnK5HJ5//nkyJGWZBeVymURMVquVrL/y+Tzm5+cp/DOTyWBnZwcnT55EKBSCw+Eg5qHVaqXViEQioYua3++nkWYikSDTlH379kGr1eK5555DIBBAtVoFABw5coRETEPcGcPi3wMkEgnZYGu1WgDAo48+il6vR6IZZildq9Vojp1IJKizXqvVyLyTadTFYjEWFhZgsViQyWRIKqvVaununM/nIRAI4Pf7yUKLqQeFQiHkcjm63S5GR0ZoVFYulzE/Pw+xWAyfz0cpPqOjoxAIBDReU6vViMVi5AfQbDZx7do1uFwuPPjgg6jX6xAIBNje3kYikcBP//RPw+/3o9Vq4Rvf+Aba7TZ6vR6mp6dx9uxZDAYDPP/884hEIti/fz8AEOeA5frJZDJ0Oh2YzWYEg0FcuXKFLjSXL1/G8vIypRap1WoMBgNMT09jeXkZoVAI3W4Xzz33HPVPZDIZtre3ceDAARw6dAj/9E//BLlcfhfPlnsHw+LfAzY3NyEUCm8L4CwUCrR0ZUSWVCpFd1cmmmFy3263CwBwOp1wOp238QNGR0fJK4AZVXAcR0t8RmLZ2dkhZaFCoYDJZEI6nUa73abgy52dHeRyOXIZYvNzp9NJxS6TybB//37IZDK88MILmJ+fh0AgoEam0WjE9vY28f9lMhl2d3exb98+WK1WKBQKGAwG/MVf/AVarRZKpRJefPFFzM7O0m/GhE+tVgt6vZ6UeyqVipiDjO0nFAqpQenxeOBwOHD58mUKB7Xb7RgbG8MLL7wAoVAIjUaDWq2GiYkJxONx+P1+mEwmiipPp9P/+ifJPYhh8e8BbP87Pz8PHo9H/velUgmjo6OQyWRIp9NUvHw+H5VKBQqFAkeOHIHT6cT6+jri8TiOHTtGnv+hUAj1ep0KlMfjoVQqURIOu7Cw0ZndbqcRWTwep16DVCqFUCikTL6XXnoJ2WwW6XQatVoNLpcLtVoNyWQSKysr0Ol0UKlUAECEHSamYXJak8kEoVCIaDRK+XyLi4uYnp4mD8Pp6Wmi9WazWczPz6NYLEIkEtF2gPUWWKf++eefJ6pzOBzG4uIibDYbWZsxTb/NZkM2mwXHcYjH45iYmCADkkwmA7vdDj6fj4MHD2J+fh7VahX5fJ7CUoe4M4bFvwe43W5sbm4SZZXNkc+dO4dWq4W1tTWo1WpKy2k2m+Q0wyKqpqen8cILLyCdTlOMdbPZJM68SqXC448/ju3tbRrLjY6OwuPxoNFoUEEBgM/nw/j4ODXiBoMBEokEVCoVisUiIpEIedn3ej2Uy2WcOnUKly5dQqlUQrFYhNPppAwApq1n0dvlchmtVguVSgXJZBJqtZq4/s888wxMJhNSqRRCoRDUajWUSiX1QeRyOfbv349qtQqpVIpgMEihnKyxmM1m8f3vfx+BQADJZBIXLlygeDLmXQCA8v2Yk+/o6CjN8tmx6fV6ag6WSiW6aAxxZwyLfw84cuQI/H4/EWCYvTZz0mWmmYwIw/bPzWYTS0tLdLdWKpUUeS0UCknRxxpvxWIR/X4fHo8H8XgcsVgMo6OjtMVgybW9Xg8qlYpWGcwmLBgM4urVq4hEIjhz5gyx/Or1Orn5Mrbe1tYWJicnMTs7S3P9kydPotFowOFwQCQSYWtrC+l0mrwGWQLu2toaOp0O+QbE43G60LDQkXa7TXf4drsNt9sN4FbicSQSIVMQNn3IZrNQKpWYmZlBt9uFWq2GRqMhr0A2Ndm3bx80Gg1xB9jYc3R0FF6vFwsLC3SRHOK1MSz+PYCFXgaDQUilUmLOMRdcJlxhk4BcLkdpvMeOHUMmk6HGoMvlIm49a2SxffWlS5fIkGN0dJS6/uzOXKvVbuO2i8VimEwmcsSJx+OQy+UkgJmbm8Nzzz2HTCaDr371qygUCmi1WlCr1RQ7Pjc3B6PRCJVKhYmJCayursJoNILjODidTlSrVQiFQhrjMTYicwNWqVTk5isQCKjzLhQK0Wg0AAA2m43YgBzHYWpqCp1OB1arFZVKBUqlEkqlEj6fD+fOnSPpcqvVwtbWFvbt2weHwwE+n498Po9MJoN2uw2/349yuYy1tTVaaTD58RB3xrD494CLFy9ibm4OLpeLDDEZq4w1l1QqFRqNBvL5PDY2Nij5ViwWw+VykXnm9evXaSsQi8Vor67T6bC9vU1GG2KxGDKZjLrezWYT7XYbCoWCYrvS6TSCwSDy+TxFhrPRWrFYJL87Jsjx+/3UBFQqlZTqYzQasbW1he3tbdrf83g8yOVy2Gw2mM1mOi7maWA0GjE3N4dUKoVwOEzhIOw7zc3Nod1uY2FhgXj+rVYL586dQ7PZJC0E0xi4XC643W5UKhU8/fTT0Gg0OHnyJN761rfCarWSvFetVpNtGWtInjt3DouLizTmUygUd/mMuTcwLP49gHnlsYK/cuUKdnd3yfBCIpGgUqkgkUjg6tWrsNlseNvb3kZJO41GA4VCAXK5HKOjo4hGo1AqlZibm8P29ja5z5hMJkxMTFATUKfToVwug8/nIx6Pw2q1QiqVIhaLkRXXYDAAx3E09pNKpXC73UgkElhYWIBAIMD09DStJBhX4E1vehP6/T5efPFFeDwe+P1+BINBkiOLRCJIpVJK9wGA0dFRkuu+6U1vojHi0tISdnZ2KJ+AWXU/+OCD8Hg8uHLlCin3TCYTLl68CB6PB6/XC7PZjHK5DK1Wi1wuh+9+97tIp9OUPMx+G+YrwOfzcejQIeTzedTrdXS7XUSjUTidTloxsMnKEK+NYfHvATMzM+h0Orh48SLdLaVSKc2yK5UK7dvdbjfkcjn56DN5bjqdht1uh1gshsPhwPXr1wEA+/btw/Hjx2m/LxaLsby8TJZbMpmMnIOazSax2Hw+Hxl8Hj58GFeuXCGOvMViQSQSgVQqRb1eh8PhwP79+xGNRikUc2Njg/bXm5ubeOtb3wqfz4fl5WXw+Xyk02nk83nS8yuVSkrFYf78jCr80EMP4cknn6QLVb1eh16vRzAYBADKEYjFYnjqqadgMBgwNzcHh8OBzc1NmnoUi0XMzMzA4XCg2+2SnoD9qdVqREuu1WpE+WVsR61WS06/Q9wZw+LfA5xOJ1KpFNrtNkwmE/bt20cNOmYewZJ83W434vE4dnZ2iNJqNBqRy+XIyYaZajLt+cGDB8mymvHim80mVlZWKGXX4XBgdXUVrVaL7Ln5fD4mJiZw/PhxuN1uPPfcc4jFYigUCuQEdOzYMUilUjz//PN0zPl8Ht/73vdQLBZx4sQJZDIZPPfcc8SL39nZQTgcRiwWw/j4OMbHx5FIJKDT6SCXy7G+vo4LFy6QrTaPx6MtikKhgEAgwJEjR/DCCy+gUCjA4XAAAHH2nU4nZDIZxGIxbTPEYjHW19fRarVICZhKpaBWqykf4MaNG1hcXCQiFfNX0Ov12NnZwTve8Q689a1vRSQSuctnzL2BYfHvARKJhMIhjUYjFhcXMTIygkwmQ0EXSqUSABCJRBCJRMgCWyKR0CycTQXS6TSmp6epYZZMJim8Mp/Pky8eswlTKpWQy+XUhV9YWEAqlaLIa+aJr9FoSG7sdrvB5/MhFAopHiuXy6Hb7VJMNgv93N7eJtWc3W6n/f74+DhFiff7fZrBs276zMwMvF4vPve5z6HZbJLTD5/Px7Vr18h5uFQqgcfjodVqwev1Eg253+9ToCcAokDzeDxMT0+TaSpT9l28eBG5XI5clfr9Plmc8/l8bG5uotPpoNvt4tChQ3fnZLmHMCz+PYI1p5inHguJZPFVQqEQHo8HgUAA/X4fL7zwArHcWGxVMBjE5OQkCWaazSY6nQ4p+5i1tc1mo7n+yMgICVXYctjpdGJmZgbj4+MQiUSIxWKo1WpQq9WkGGTml263G+VymYIuQ6EQbDYbpqamEI1GUavVANzK6ut0OmSYwe7MTPzDLMCMRiPkcjkeeughnDx5Evl8HhaLBcFgkHIEBQIBCoUCHQNblguFQrz//e8nB97r16/DZDJR0aZSKRw/fhylUgmnTp3C8vIypFIpLly4AB6Ph0AgQJMIFhEGAPv374dSqcTy8jJefPFFCIVCfOADH7g7J8o9hGHx7wFXrlyh0It0Og23241kMkkjPplMRmKdVqtFqj+Hw0Fjp16vR1l8AoEAzWYTW1tbZPet0+nA4/EQDAZx8OBBstEqFApIJBLodrtkmW00GhEIBMiVRy6Xo9Vqwe/3o9vt0ohteXmZosE5joNGo4HL5QKPx8PY2BjJcz0eD2nmmSy51WqRwvDUqVPkna9UKqk7n8/nsba2hkqlAolEgna7TcQmJnlmS/NGowGbzUakndXVVTIYYQ5IsViMJhcXLlygUWcgEKBmZzgcpgRjrVYLuVwOkUhEF0Yej4fLly/fzdPlnsGw+PeAfD4PtVqNfD4Pr9cLsViMkZERmm/n83k6Mbe2tmCz2XD06FFykmV3c5vNBoVCQU68uVwO6XQaCoUCJ0+epL1zLBbDzs4OOfowZiGzuZ6cnCT6rMvlQrvdxvnz55HP5yGXy/G2t72N9t2ZTIYuXEwctLi4iL/7u7+jplo+n6fm5Pz8PGQyGfx+P9RqNVKpFGQyGaLRKE6dOgUANJJst9solUpk7skUf6lUigxQmFV4JBJBr9fDiy++iF6vB7PZjMOHDyMYDCIej8NoNFLcls/ng06nQyqVwvb2NjU6hUIhXSSTySTsdjtyuRw1DbVaLTn/DHFnDIt/D1AqlVAoFJidnSXJqt1uh0AgwMLCAsRiMbLZLFKpFKLRKOXMM359o9HA0aNH0Wq1EAwGSZZ79uxZhEIhNJtNyptrt9toNpvwer3I5/PY3NwEn88n/nun08HVq1eh0+lgt9sxOTmJWCxGPQaPx4NUKgU+n0/xVk8//TSMRiPsdjsFcXa7Xfh8PvIWZEt0nU5HrsPHjx+H3+/H8vLybd54u7u7SKfTUCqVMBgMOHDgAGXmsdyC0dFRfOlLX6JswLe85S1ktvG9730PFouFLn71ep3u+lNTU6jX6/T48vIyer0eNBoNjh8/jnA4jGAwCB6Ph2q1ing8Tr8Z6x0Mi39vGBb/HlEoFNBut5HNZmEymSCRSMh3n7nv9Pt9OJ1OSKVSUvgpFApIJBIEAgFotVqEw2FYLBY0m01S0MViMYrS2tjYoEAMFsbJvPjlcjlRbYVCIc25GZfA6/VSSu/GxgbK5TLx9AOBAOLxOMVmicViBINBmM1meDweJBIJyhXQaDSo1+tYXV2FTCaj0dn29jY1Ppl0uNvtolgsYmpqCr1eDxsbG5icnEQwGKQVy+joKEZHR2G32xEMBtFsNrGzs4NUKkV6gq2tLfB4PDz//POYmJgg4ZNIJKK7/fLyMkWHOZ1OOBwOIkdJJBLUajXU63XodLq7fLbcGxgW/x4wOTkJiUSC8+fPI5fLIZFIIBqNQiKR4NixYxRIqVKpaATHAi5UKhXUajWl8bBuOgAS8LDE30KhAKVSiUKhgHq9TnRaFrbBvP2ZbFgoFGJ3dxdms5n24EwcEw6HUa/X6U+tVoPdbqc0GxbbHQgESB3ocrlIiXfkyBFsb2+jWCyi0WjA5XLh0KFDkEgkmJqawsrKCra3t5HP5ymKHAD6/T7C4TBUKhVsNhutDnw+H7H8zGYzzfYrlQpcLheEQiHW1tbQ7/fx4IMP4vjx43jmmWcgEAjg9XqxtLSE7e1tKBQKdLtdNJtNCAQCHDp0iManOp2OglGHuDN4HMdxd/sghhhiiH99DP2OhhjiDYph8Q8xxBsUw+IfYog3KIbFP8QQb1AMi3+IId6gGBb/EEO8QTEs/iGGeINiWPxDDPEGxbD4hxjiDYr/Dz8UDRKBMqGkAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_loaded)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T09:54:44.912350517Z",
     "start_time": "2023-06-19T09:54:44.114656116Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    imgs, labels = next(iter(dataloaders['val']))\n",
    "    imgs = imgs.to(device)\n",
    "    outputs = model_loaded(imgs)\n",
    "    wtf, preds = torch.max(outputs,1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T10:07:17.798079476Z",
     "start_time": "2023-06-19T10:07:17.379789198Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor(0.8954, device='cuda:0')"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def eval_model(model):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        correct = 0\n",
    "        for imgs, labels in dataloaders['val']:\n",
    "            imgs = imgs.to(device)\n",
    "            outputs = model(imgs)\n",
    "            _, preds = torch.max(outputs,1)\n",
    "            correct += sum(labels.to(device) == preds)\n",
    "    return correct / dataset_sizes['val']\n",
    "eval_model(model_loaded)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T10:27:38.090192162Z",
     "start_time": "2023-06-19T10:27:37.350113678Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "tensor(6, device='cuda:0')"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 + sum(labels.to(device) == preds) + sum(labels.to(device) == preds)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T10:22:43.290971787Z",
     "start_time": "2023-06-19T10:22:43.273912531Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "153"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_sizes['val']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-19T10:29:40.879056223Z",
     "start_time": "2023-06-19T10:29:40.871928437Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
